{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-0",
   "metadata": {},
   "source": [
    "# Compare Stepwise Group-Wise Regression Results\n",
    "\n",
    "This notebook compares the four result files produced by\n",
    "`StepwiseGroupWiseRegression.ipynb`, which differ along two dimensions:\n",
    "\n",
    "| Dimension | Values |\n",
    "|---|---|\n",
    "| **Grouping level** | macro (55 ACS table groups) vs micro (865 sub-groups) |\n",
    "| **Response mode** | individual (per-DV selection) vs aggregate (mean Adj R²) |\n",
    "\n",
    "### Sections\n",
    "\n",
    "1. Load results\n",
    "2. Summary table — final Adj R² for every configuration\n",
    "3. Adj R² progression curves\n",
    "4. Parsimony — Adj R² vs cumulative predictor count\n",
    "5. Micro sub-group commonality — Individual vs Aggregate\n",
    "   - 5a. Set overlap statistics + Jaccard similarity\n",
    "   - 5b. Selection order correlation (Spearman ρ)\n",
    "   - 5c. Cross-response overlap within micro individual\n",
    "   - 5d. Unique sub-groups — individual-only vs aggregate-only\n",
    "6. Micro-Individual cutoff analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell-1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-2",
   "metadata": {},
   "source": [
    "## 1. Load results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cell-3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows per file:\n",
      "  macro_individual            30 rows\n",
      "  macro_aggregate             12 rows\n",
      "  micro_individual           250 rows\n",
      "  micro_aggregate             73 rows\n"
     ]
    }
   ],
   "source": [
    "macro_ind = pl.read_csv(\"Results_macro_individual.csv\")\n",
    "macro_agg = pl.read_csv(\"Results_macro_aggregate.csv\")\n",
    "micro_ind = pl.read_csv(\"Results_micro_individual.csv\")\n",
    "micro_agg = pl.read_csv(\"Results_micro_aggregate.csv\")\n",
    "\n",
    "RESPONSES = [\"BEV Count\", \"BEV Proportion\", \"BEV per Capita\"]\n",
    "\n",
    "print(\"Rows per file:\")\n",
    "for name, df in [(\"macro_individual\", macro_ind), (\"macro_aggregate\", macro_agg),\n",
    "                  (\"micro_individual\", micro_ind), (\"micro_aggregate\", micro_agg)]:\n",
    "    print(f\"  {name:25s} {df.height:>4d} rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-4",
   "metadata": {},
   "source": [
    "## 2. Summary table — final Adj R² for every configuration\n",
    "\n",
    "For each (grouping × response) combination, extract the final step’s Adj R²,\n",
    "along with the number of groups and predictors used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cell-5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (12, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Grouping</th><th>Mode</th><th>Response</th><th>Steps</th><th>Groups</th><th>Predictors</th><th>Adj R²</th></tr><tr><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;macro&quot;</td><td>&quot;aggregate&quot;</td><td>&quot;BEV Count&quot;</td><td>12</td><td>12</td><td>237</td><td>0.958565</td></tr><tr><td>&quot;macro&quot;</td><td>&quot;individual&quot;</td><td>&quot;BEV Count&quot;</td><td>8</td><td>8</td><td>358</td><td>0.960864</td></tr><tr><td>&quot;micro&quot;</td><td>&quot;aggregate&quot;</td><td>&quot;BEV Count&quot;</td><td>73</td><td>73</td><td>527</td><td>0.999399</td></tr><tr><td>&quot;micro&quot;</td><td>&quot;individual&quot;</td><td>&quot;BEV Count&quot;</td><td>82</td><td>82</td><td>524</td><td>0.999856</td></tr><tr><td>&quot;macro&quot;</td><td>&quot;aggregate&quot;</td><td>&quot;BEV Proportion&quot;</td><td>12</td><td>12</td><td>237</td><td>0.841301</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;micro&quot;</td><td>&quot;individual&quot;</td><td>&quot;BEV Proportion&quot;</td><td>79</td><td>79</td><td>527</td><td>0.999976</td></tr><tr><td>&quot;macro&quot;</td><td>&quot;aggregate&quot;</td><td>&quot;BEV per Capita&quot;</td><td>12</td><td>12</td><td>237</td><td>0.790288</td></tr><tr><td>&quot;macro&quot;</td><td>&quot;individual&quot;</td><td>&quot;BEV per Capita&quot;</td><td>14</td><td>14</td><td>241</td><td>0.791587</td></tr><tr><td>&quot;micro&quot;</td><td>&quot;aggregate&quot;</td><td>&quot;BEV per Capita&quot;</td><td>73</td><td>73</td><td>527</td><td>0.999859</td></tr><tr><td>&quot;micro&quot;</td><td>&quot;individual&quot;</td><td>&quot;BEV per Capita&quot;</td><td>89</td><td>89</td><td>527</td><td>0.999981</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (12, 7)\n",
       "┌──────────┬────────────┬────────────────┬───────┬────────┬────────────┬──────────┐\n",
       "│ Grouping ┆ Mode       ┆ Response       ┆ Steps ┆ Groups ┆ Predictors ┆ Adj R²   │\n",
       "│ ---      ┆ ---        ┆ ---            ┆ ---   ┆ ---    ┆ ---        ┆ ---      │\n",
       "│ str      ┆ str        ┆ str            ┆ i64   ┆ i64    ┆ i64        ┆ f64      │\n",
       "╞══════════╪════════════╪════════════════╪═══════╪════════╪════════════╪══════════╡\n",
       "│ macro    ┆ aggregate  ┆ BEV Count      ┆ 12    ┆ 12     ┆ 237        ┆ 0.958565 │\n",
       "│ macro    ┆ individual ┆ BEV Count      ┆ 8     ┆ 8      ┆ 358        ┆ 0.960864 │\n",
       "│ micro    ┆ aggregate  ┆ BEV Count      ┆ 73    ┆ 73     ┆ 527        ┆ 0.999399 │\n",
       "│ micro    ┆ individual ┆ BEV Count      ┆ 82    ┆ 82     ┆ 524        ┆ 0.999856 │\n",
       "│ macro    ┆ aggregate  ┆ BEV Proportion ┆ 12    ┆ 12     ┆ 237        ┆ 0.841301 │\n",
       "│ …        ┆ …          ┆ …              ┆ …     ┆ …      ┆ …          ┆ …        │\n",
       "│ micro    ┆ individual ┆ BEV Proportion ┆ 79    ┆ 79     ┆ 527        ┆ 0.999976 │\n",
       "│ macro    ┆ aggregate  ┆ BEV per Capita ┆ 12    ┆ 12     ┆ 237        ┆ 0.790288 │\n",
       "│ macro    ┆ individual ┆ BEV per Capita ┆ 14    ┆ 14     ┆ 241        ┆ 0.791587 │\n",
       "│ micro    ┆ aggregate  ┆ BEV per Capita ┆ 73    ┆ 73     ┆ 527        ┆ 0.999859 │\n",
       "│ micro    ┆ individual ┆ BEV per Capita ┆ 89    ┆ 89     ┆ 527        ┆ 0.999981 │\n",
       "└──────────┴────────────┴────────────────┴───────┴────────┴────────────┴──────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_rows = []\n",
    "\n",
    "# --- Individual mode: last step per response ---\n",
    "for grouping, df in [(\"macro\", macro_ind), (\"micro\", micro_ind)]:\n",
    "    for resp in RESPONSES:\n",
    "        sub = df.filter(pl.col(\"Response\") == resp)\n",
    "        last = sub.row(-1, named=True)\n",
    "        summary_rows.append({\n",
    "            \"Grouping\": grouping,\n",
    "            \"Mode\": \"individual\",\n",
    "            \"Response\": resp,\n",
    "            \"Steps\": last[\"Step\"],\n",
    "            \"Groups\": last[\"Cumul. Groups\"],\n",
    "            \"Predictors\": last[\"Cumul. Predictors\"],\n",
    "            \"Adj R²\": last[\"Adj R\\u00b2\"],\n",
    "        })\n",
    "\n",
    "# --- Aggregate mode: last step, per-response R² from dedicated columns ---\n",
    "for grouping, df in [(\"macro\", macro_agg), (\"micro\", micro_agg)]:\n",
    "    last = df.row(-1, named=True)\n",
    "    for resp in RESPONSES:\n",
    "        summary_rows.append({\n",
    "            \"Grouping\": grouping,\n",
    "            \"Mode\": \"aggregate\",\n",
    "            \"Response\": resp,\n",
    "            \"Steps\": last[\"Step\"],\n",
    "            \"Groups\": last[\"Cumul. Groups\"],\n",
    "            \"Predictors\": last[\"Cumul. Predictors\"],\n",
    "            \"Adj R²\": last[f\"Adj R\\u00b2 ({resp})\"],\n",
    "        })\n",
    "\n",
    "summary = pl.DataFrame(summary_rows).sort(\"Response\", \"Grouping\", \"Mode\")\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-6",
   "metadata": {},
   "source": [
    "### Summary heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cell-7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pivot into a matrix: rows = Response, cols = (Grouping, Mode)\n",
    "configs = [\"macro / individual\", \"macro / aggregate\",\n",
    "           \"micro / individual\", \"micro / aggregate\"]\n",
    "matrix = np.zeros((len(RESPONSES), len(configs)))\n",
    "pred_counts = np.zeros_like(matrix, dtype=int)\n",
    "\n",
    "for row in summary.iter_rows(named=True):\n",
    "    ri = RESPONSES.index(row[\"Response\"])\n",
    "    ci = configs.index(f\"{row['Grouping']} / {row['Mode']}\")\n",
    "    matrix[ri, ci] = row[\"Adj R\\u00b2\"]\n",
    "    pred_counts[ri, ci] = row[\"Predictors\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 3.5))\n",
    "im = ax.imshow(matrix, cmap=\"YlGn\", vmin=0.7, vmax=1.0, aspect=\"auto\")\n",
    "\n",
    "ax.set_xticks(range(len(configs)), configs, fontsize=10)\n",
    "ax.set_yticks(range(len(RESPONSES)), RESPONSES, fontsize=10)\n",
    "\n",
    "for i in range(len(RESPONSES)):\n",
    "    for j in range(len(configs)):\n",
    "        val = matrix[i, j]\n",
    "        preds = pred_counts[i, j]\n",
    "        color = \"white\" if val > 0.95 else \"black\"\n",
    "        ax.text(j, i, f\"{val:.4f}\\n({preds} vars)\",\n",
    "                ha=\"center\", va=\"center\", fontsize=9, color=color)\n",
    "\n",
    "ax.set_title(\"Final Adj R\\u00b2 by Configuration\", fontsize=12, pad=10)\n",
    "fig.colorbar(im, ax=ax, label=\"Adj R\\u00b2\", shrink=0.8)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-8",
   "metadata": {},
   "source": [
    "## 3. Adj R² progression curves\n",
    "\n",
    "Plot Adj R² vs step number with one subplot per parameter combination\n",
    "(macro/micro × individual/aggregate). Each subplot overlays the three\n",
    "response variables so their relative difficulty can be compared within\n",
    "a single configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cell-9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "COLORS = {\"BEV Count\": \"#1f77b4\", \"BEV Proportion\": \"#ff7f0e\", \"BEV per Capita\": \"#2ca02c\"}\n",
    "\n",
    "configs = [\n",
    "    (\"Macro / Individual\", macro_ind, \"individual\"),\n",
    "    (\"Macro / Aggregate\",  macro_agg, \"aggregate\"),\n",
    "    (\"Micro / Individual\", micro_ind, \"individual\"),\n",
    "    (\"Micro / Aggregate\",  micro_agg, \"aggregate\"),\n",
    "]\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 9))\n",
    "\n",
    "for ax, (title, df, mode) in zip(axes.flat, configs):\n",
    "    for resp in RESPONSES:\n",
    "        if mode == \"individual\":\n",
    "            sub = df.filter(pl.col(\"Response\") == resp)\n",
    "            steps = sub[\"Step\"].to_list()\n",
    "            r2 = sub[\"Adj R\\u00b2\"].to_list()\n",
    "        else:\n",
    "            steps = df[\"Step\"].to_list()\n",
    "            r2 = df[f\"Adj R\\u00b2 ({resp})\"].to_list()\n",
    "\n",
    "        ax.plot(steps, r2, \"o-\", markersize=4, label=resp, color=COLORS[resp])\n",
    "\n",
    "    ax.set_title(title, fontsize=12)\n",
    "    ax.set_xlabel(\"Step\")\n",
    "    ax.set_ylabel(\"Adj R\\u00b2\")\n",
    "    ax.yaxis.set_major_formatter(mtick.FormatStrFormatter(\"%.2f\"))\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    ax.legend(fontsize=8, loc=\"lower right\")\n",
    "\n",
    "fig.suptitle(\"Adj R\\u00b2 Progression by Step\", fontsize=14, y=1.01)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-10",
   "metadata": {},
   "source": [
    "## 4. Parsimony — Adj R² vs cumulative predictor count\n",
    "\n",
    "The step axis hides a key difference: micro groups add far fewer predictors\n",
    "per step than macro groups. Plotting against cumulative predictors reveals\n",
    "each configuration's efficiency at converting model complexity into fit.\n",
    "One subplot per parameter combination, with response variables as series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell-11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(14, 9))\n",
    "\n",
    "for ax, (title, df, mode) in zip(axes.flat, configs):\n",
    "    for resp in RESPONSES:\n",
    "        if mode == \"individual\":\n",
    "            sub = df.filter(pl.col(\"Response\") == resp)\n",
    "            preds = sub[\"Cumul. Predictors\"].to_list()\n",
    "            r2 = sub[\"Adj R\\u00b2\"].to_list()\n",
    "        else:\n",
    "            preds = df[\"Cumul. Predictors\"].to_list()\n",
    "            r2 = df[f\"Adj R\\u00b2 ({resp})\"].to_list()\n",
    "\n",
    "        ax.plot(preds, r2, \"o-\", markersize=4, label=resp, color=COLORS[resp])\n",
    "\n",
    "    ax.set_title(title, fontsize=12)\n",
    "    ax.set_xlabel(\"Cumulative Predictors\")\n",
    "    ax.set_ylabel(\"Adj R\\u00b2\")\n",
    "    ax.yaxis.set_major_formatter(mtick.FormatStrFormatter(\"%.2f\"))\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    ax.legend(fontsize=8, loc=\"lower right\")\n",
    "\n",
    "fig.suptitle(\"Adj R\\u00b2 vs Cumulative Predictors (Parsimony)\", fontsize=14, y=1.01)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4esngwtajl",
   "metadata": {},
   "source": [
    "## 5. Micro sub-group commonality — Individual vs Aggregate\n",
    "\n",
    "The two micro configurations select from the same pool of 865 sub-groups but\n",
    "use different objectives: **individual** optimises Adj R² for one DV at a time,\n",
    "while **aggregate** maximises the mean Adj R² across all three DVs.\n",
    "\n",
    "This section analyses:\n",
    "- How many sub-groups are shared between each individual selection and the\n",
    "  aggregate selection\n",
    "- Whether shared sub-groups are selected in a similar order (rank correlation)\n",
    "- Which sub-groups are unique to each approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdi7i9333qh",
   "metadata": {},
   "source": [
    "### 5a. Set overlap statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "mb5xgmoa14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Pairwise overlap between micro selections ===\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (12, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>A</th><th>B</th><th>|A|</th><th>|B|</th><th>Shared</th><th>Jaccard</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Ind: BEV Count&quot;</td><td>&quot;Ind: BEV Proportion&quot;</td><td>82</td><td>79</td><td>19</td><td>0.134</td></tr><tr><td>&quot;Ind: BEV Count&quot;</td><td>&quot;Ind: BEV per Capita&quot;</td><td>82</td><td>89</td><td>17</td><td>0.11</td></tr><tr><td>&quot;Ind: BEV Count&quot;</td><td>&quot;Aggregate&quot;</td><td>82</td><td>73</td><td>22</td><td>0.165</td></tr><tr><td>&quot;Ind: BEV Proportion&quot;</td><td>&quot;Ind: BEV Count&quot;</td><td>79</td><td>82</td><td>19</td><td>0.134</td></tr><tr><td>&quot;Ind: BEV Proportion&quot;</td><td>&quot;Ind: BEV per Capita&quot;</td><td>79</td><td>89</td><td>24</td><td>0.167</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Ind: BEV per Capita&quot;</td><td>&quot;Ind: BEV Proportion&quot;</td><td>89</td><td>79</td><td>24</td><td>0.167</td></tr><tr><td>&quot;Ind: BEV per Capita&quot;</td><td>&quot;Aggregate&quot;</td><td>89</td><td>73</td><td>30</td><td>0.227</td></tr><tr><td>&quot;Aggregate&quot;</td><td>&quot;Ind: BEV Count&quot;</td><td>73</td><td>82</td><td>22</td><td>0.165</td></tr><tr><td>&quot;Aggregate&quot;</td><td>&quot;Ind: BEV Proportion&quot;</td><td>73</td><td>79</td><td>25</td><td>0.197</td></tr><tr><td>&quot;Aggregate&quot;</td><td>&quot;Ind: BEV per Capita&quot;</td><td>73</td><td>89</td><td>30</td><td>0.227</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (12, 6)\n",
       "┌─────────────────────┬─────────────────────┬─────┬─────┬────────┬─────────┐\n",
       "│ A                   ┆ B                   ┆ |A| ┆ |B| ┆ Shared ┆ Jaccard │\n",
       "│ ---                 ┆ ---                 ┆ --- ┆ --- ┆ ---    ┆ ---     │\n",
       "│ str                 ┆ str                 ┆ i64 ┆ i64 ┆ i64    ┆ f64     │\n",
       "╞═════════════════════╪═════════════════════╪═════╪═════╪════════╪═════════╡\n",
       "│ Ind: BEV Count      ┆ Ind: BEV Proportion ┆ 82  ┆ 79  ┆ 19     ┆ 0.134   │\n",
       "│ Ind: BEV Count      ┆ Ind: BEV per Capita ┆ 82  ┆ 89  ┆ 17     ┆ 0.11    │\n",
       "│ Ind: BEV Count      ┆ Aggregate           ┆ 82  ┆ 73  ┆ 22     ┆ 0.165   │\n",
       "│ Ind: BEV Proportion ┆ Ind: BEV Count      ┆ 79  ┆ 82  ┆ 19     ┆ 0.134   │\n",
       "│ Ind: BEV Proportion ┆ Ind: BEV per Capita ┆ 79  ┆ 89  ┆ 24     ┆ 0.167   │\n",
       "│ …                   ┆ …                   ┆ …   ┆ …   ┆ …      ┆ …       │\n",
       "│ Ind: BEV per Capita ┆ Ind: BEV Proportion ┆ 89  ┆ 79  ┆ 24     ┆ 0.167   │\n",
       "│ Ind: BEV per Capita ┆ Aggregate           ┆ 89  ┆ 73  ┆ 30     ┆ 0.227   │\n",
       "│ Aggregate           ┆ Ind: BEV Count      ┆ 73  ┆ 82  ┆ 22     ┆ 0.165   │\n",
       "│ Aggregate           ┆ Ind: BEV Proportion ┆ 73  ┆ 79  ┆ 25     ┆ 0.197   │\n",
       "│ Aggregate           ┆ Ind: BEV per Capita ┆ 73  ┆ 89  ┆ 30     ┆ 0.227   │\n",
       "└─────────────────────┴─────────────────────┴─────┴─────┴────────┴─────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build sub-group sets for each micro configuration\n",
    "micro_sets: dict[str, set[str]] = {}\n",
    "micro_order: dict[str, dict[str, int]] = {}  # group → step\n",
    "\n",
    "for resp in RESPONSES:\n",
    "    sub = micro_ind.filter(pl.col(\"Response\") == resp)\n",
    "    key = f\"Ind: {resp}\"\n",
    "    micro_sets[key] = set(sub[\"Group Added\"].to_list())\n",
    "    micro_order[key] = dict(zip(sub[\"Group Added\"].to_list(), sub[\"Step\"].to_list()))\n",
    "\n",
    "micro_sets[\"Aggregate\"] = set(micro_agg[\"Group Added\"].to_list())\n",
    "micro_order[\"Aggregate\"] = dict(zip(micro_agg[\"Group Added\"].to_list(), micro_agg[\"Step\"].to_list()))\n",
    "\n",
    "labels = list(micro_sets.keys())\n",
    "\n",
    "# Pairwise overlap table\n",
    "overlap_rows = []\n",
    "for a in labels:\n",
    "    for b in labels:\n",
    "        shared = micro_sets[a] & micro_sets[b]\n",
    "        union = micro_sets[a] | micro_sets[b]\n",
    "        overlap_rows.append({\n",
    "            \"A\": a,\n",
    "            \"B\": b,\n",
    "            \"|A|\": len(micro_sets[a]),\n",
    "            \"|B|\": len(micro_sets[b]),\n",
    "            \"Shared\": len(shared),\n",
    "            \"Jaccard\": round(len(shared) / len(union), 3) if union else 0.0,\n",
    "        })\n",
    "\n",
    "overlap_df = pl.DataFrame(overlap_rows)\n",
    "print(\"=== Pairwise overlap between micro selections ===\")\n",
    "overlap_df.filter(pl.col(\"A\") != pl.col(\"B\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "h757tiqrcqh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xohInkUgghMgzoXLjxo157pTp2bMnTpw4gbt37xZYXs+ePfHff/9Ju9QLOqeKigqUlZWlaampqdi2bZvc9e7SpQtu3ryJf//9Vybd399fruPz+wcPQDoEZmJiInddSotEIsnzuly/fh3BwcHFKje3zPx6rYCcCc5qampwdnbG3bt35RrasbW1RZUqVfDbb7/JpEdEROCff/5Bt27dilRXeV+n7t27Q0VFBQ8ePICNjU2+W0HkPbZhw4aoV68eNm/enO8/5FwFXV95z9OoUSMYGxsjICBA5i7TJ0+e4Ny5cx+6ZEhKSkJycnK++969brmTxq9fvy6Tr6AbGZKSkvLs8/f3h5KSEjp16iSt/9ttyj1H586dASDP+ls7duz4YJty5b6P3n2f7dq1CykpKTLvMwsLizzt+ueffwq8Ntu3b5f5/dy5c3jy5Em+d79KJBJYWVlh1apV0NPTw9WrV+VuA5Ut9jhRiZJIJNDR0UGnTp2wfPlyGBoawsLCAqdOncKmTZugp6cnk3/hwoU4fPgwOnXqhDlz5qBZs2aIj4/HX3/9henTp6Nx48aYOnUqAgMD0a9fP8yePRtt2rRBamoqTp06hT59+qBLly7o3bs3Vq5ciREjRuCLL75AXFwcfvzxx0LdDTN16lRs3rwZvXv3hpeXl/SuureXPHif7t27o3bt2ujbty8aN26M7OxshISEYMWKFdDW1saUKVMKcylLRZ8+fbBo0SJ4eHigc+fOuHv3LhYuXIg6deogMzOzyOU2a9YMALB69WqMGTMGqqqqaNSokXQRSj09PYwePRq+vr4wNzdH3759P1imnp4evvvuO8yZMwejR4/G8OHDERcXhwULFkBDQwMeHh5Fqqu8r5OFhQUWLlyIuXPn4uHDh+jRoweqVauGZ8+e4eLFi9DS0pJZ0+hthTl27dq16Nu3L9q1a4dp06bBzMwMYWFhOHLkiPQfb0HXV97zKCkpYdGiRXB1dcWAAQPw+eefIz4+Hp6ennIN1d29exfdu3fHsGHD0LlzZxgbG+Ply5c4ePAgNmzYAHt7e+lcqdatW6NRo0aYOXMmMjMzUa1aNezZswdnzpzJt2wDAwNMnDgRYWFhaNiwIQ4dOoRffvkFEydOlN4hW5AePXqgffv2mDFjBhITE9GqVSsEBwdLl1n40N2CAODg4IDu3btj1qxZSExMRPv27aV31VlbW2PUqFHSvKNGjcJ3332H+fPno3Pnzrh16xbWrFkDXV3dfMu+fPkyXF1dMXjwYISHh2Pu3LmoVasW3NzcAAAHDhyAj48P+vfvj7p160IIgd27dyM+Ph4ODg4frDuVk3Kdmk6Vxtq1awUAERoaKoQQIiIiQjg5OYlq1aqJqlWrih49eogbN27kuftECCHCw8PF+PHjRc2aNYWqqqowMTERQ4YMEc+ePZPmefnypZgyZYowMzMTqqqqokaNGqJ3797izp070jybN28WjRo1Eurq6qJu3bpiyZIlYtOmTXnu8DE3Nxe9e/fOtx23bt0SDg4OQkNDQ+jr6wsXFxexb98+ue6qCwwMFCNGjBANGjQQ2traQlVVVZiZmYlRo0bluWuwoLvqli9fLpMv906knTt3yqTndxeZPHfVpaWliZkzZ4patWoJDQ0N0bJlS7F3714xZswYmTurCqrP2/vevqtOCCHc3d2FiYmJUFJSyvd6nTx5UgAQP/zwQ54y32fjxo2iefPmQk1NTejq6op+/fqJmzdvfvB6FKQwr5MQQuzdu1d06dJF6OjoCHV1dWFubi4GDRokjh8/Ls3z7l11hTlWCCGCg4NFz549ha6urlBXVxf16tUT06ZNk8nzvusr73k2btwoGjRoINTU1ETDhg3F5s2b87z2+Xn58qXw8vISXbt2FbVq1RJqampCS0tLtGjRQnh5eYlXr17J5P/vv/+Eo6Oj0NHREdWrVxdff/219A64d++qa9q0qTh58qSwsbER6urqwtjYWMyZM0dkZGS8t065Xrx4IcaNGyf09PSEpqamcHBwEOfPnxcAZO7gzX2Nnj9/nqeM1NRUMWvWLGFubi5UVVWFsbGxmDhxonj58qVMvrS0NPHtt98KU1NTUaVKFdG5c2cREhJS4F11R48eFaNGjRJ6enrSO0Tv3bsnzXfnzh0xfPhwUa9ePVGlShWhq6sr2rRpI/z8/ORqO5UPiRDvrA5IVARTpkzBmjVrEB8fX+CjLkixzZgxA76+vggPD5fOXSEqDf7+/nB2dsbZs2flvmuwJOWumXXp0qX3DunSx4lDdVQsV65cwaVLl7B582Z89tlnDJooj/Pnz+O///6Dj48PvvzySwZNVKICAgLw9OlTNGvWDEpKSjh//jyWL1+OTp06lUvQRJUfAycqlkGDBiEhIQGfffYZfvrpp/KuDlVAtra20NTURJ8+feDl5VXe1aFKpmrVqtixYwe8vLyQkpICY2NjjB07lu81KjUcqiMiIiKSE5cjICIiIpITAyciIiIiOTFwIiIiIpITAyciIiIiOTFwIiIiIpITAyciIiIiOTFwIiIiIpITAyciIiIiOXHlcCq07OxsREZGomrVqpBIJOVdHSKiSk8IgaSkJJiYmEBJiX0e5YmBExVaZGQkTE1Ny7saREQKJzw8HLVr1y7vaig0Bk5UaLkP8p2GOlDnaG+pmP1wWnlXodKSKKuXdxUqNfH6RXlXoVJKTH4Ns1YefJB6BcDAiQotd3hOHUrQgHI516Zy0qlapbyrUGlJVDTKuwqVmlBNLe8qVGqcHlH+2F1AREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREJCcGTkRERERyYuBEREREpe706dPo27cvTExMIJFIsHfv3g8ec+rUKbRq1QoaGhqoW7cu1q1bV/oV/QAGTkRERFTqUlJSYGVlhTVr1siV/9GjR+jVqxc6duyIa9euYc6cOZg8eTJ27dpVyjV9P5VyPTsRERGVq9evXyM9Pb1IxwohIJFIZNLU1dWhrq6eJ2/Pnj3Rs2dPuctet24dzMzM4O3tDQBo0qQJLl++jB9//BFOTk5Fqm9JYOBERESkoF6/fo3qVbSRjKwiHa+trY3k5GSZNA8PD3h6eha7bsHBwXB0dJRJ6969OzZt2oSMjAyoqqoW+xxFwcCJiIhIQaWnpyMZWZiBOlAv5OydNGRjRfIjhIeHQ0dHR5qeX29TUURHR8PIyEgmzcjICJmZmYiNjYWxsXGJnKewGDgREREpuCpQggaUC3VMbpilo6MjEziVpHeHAYUQ+aaXJQZORERECk4Jhb9brLTvLqtZsyaio6Nl0mJiYqCiogIDA4NSPnvBeFcdfVTMOtpg2J++mP40CB7iLhr16/bBY8w7tcbnl3dhbup1TH5wHK2+HJYnT5OBjnC7eRBzX4fC7eZBNO7/aWlU/6Pgs/kf1G01C1VqfwmbbgsRFPxfgXmjouPh/OUGNG43B8o1XDF1bkCePLsPXEHrTxeiWr2voG0+Edb2ntj2+7nSbEKF5bPxGOpYTYVGzbFoZT8XQefuFJg3KvolRriuQaPWM6GkPxJT3bflyXPzdgScRnvDovkUSKo5w9v3cGlWv8Lz8QtC3baeqFJnOmy6L0PQhQcF5o16lgBnt1/RuIMXlGtNwdT5ee/U6uL0E5RMJufZ+owq/1viFYGtrS2OHTsmk3b06FHY2NiU2/wmoJIHTi1atICfn195V4NKkJqWJp79exeHvlooV349i9oYcWgDwoKuYL11fwQtXoeeP81Fk4FvJhzWbtcCgwJX4fq2fVhn1Q/Xt+3DoN+9UatN89JqRoUVuOcips3bgTlTe+PqPx7o0K4Beg3zRlhEXL7509IzYWigjTnT+sCqae188+hX08KcaX1w7vAc/HtyAcYOb4/xk7fgyD83SrMpFU7g7mBMnbMNc2f0w7VT36OjbWP0HLIMYeGx+eZPS89EdUMdzJ3RD1aWZvnmeZWahrrmNfCDxzDUNNIrxdpXfIH7rmKax27MmeyIq0e/RYe29dDL2RdhES/yzS99705xhNUnJvnm2bXRBZEhXtIt9IQ7lJWVMKiPdWk2pVwoFXErjOTkZISEhCAkJARAznIDISEhCAsLAwC4u7tj9OjR0vwTJkzAkydPMH36dNy+fRubN2/Gpk2bMHPmzKI3tARU6MDJ3t5eehtiWZBIJNDU1IS2tjb09PRgb28vfYEBwM/PD8rKytDW1pbZdu3ahaVLl8LS0jLfctu3bw8PD48Cz7tlyxbY2NhAW1sbRkZG6Ny5M3bu3FnSzcuXRCKRaWNFd/+v0zjxnTfu7Dn24cwAbCYMQ0JYFI5MW4zYOw9xbdMfuLZ5N2xnjpfmaTt1DB4cO4czP2xA3N2HOPPDBjz6+zzaTh1TWs2osFatO4rxzh3hOqoTmjQ0gff3w2FaSx++W07mm9/CzBCrF4/A6KF20NXRzDePffvGGNC7JZo0NEG9OjUw5UsHNP+kNs5cuFeKLal4VvochstIe7iO7oImjWrBe8komNYygO/m4/nmtzCrjtU/jMboYR0LvLatW9bD8kUjMMzJFupqij3zYtWGExg/vB1cne3QpEFNeC90gqlJNfhuPZNvfgtTA6xe5ITRg9tAV6dKvnn0q2mhZg0d6Xbs9B1oVlHF4L4tSrEl5aMsAqfLly/D2toa1tY5gef06dNhbW2N+fPnAwCioqKkQRQA1KlTB4cOHcLJkyfRokULLFq0CD/99FO5LkUAVPDAqTycO3cOycnJeP78Odq3b4/BgwfL7G/WrBmSk5NlNicnJ4wdOxZ3797FxYsXZfLfvXsX58+fx7hx4/I93+zZszF//nx4eHjg2bNniIyMxIIFC7Bnz55Sa6MiqW3bAg+PnpVJe3AkCCY2llBSyflHY2rbAg+PnsmTx9Su8n2rfJ/09Exc+fcJHO2byqQ72H+C4Ev3S+QcQgj8ffoW7j6IRifbhiVS5scgPT0TV0IewbFrM5l0xy7NcO6iYgWQpSE9PRNXrofDsXNjmXSHzo0RfPlRiZ1nc8B5DO3XClqaJXPXWEUiKeJWGPb29hBC5NlyR4b8/Pxw8uRJmWM6d+6Mq1evIi0tDY8ePcKECROK2sQS89EETidPnoSenh42btwIU1NTGBgY4Ntvv5XJs2bNGum+uXPnFut8qqqqcHZ2xv379+VaGMzIyAi9e/fGli1bZNK3bNmCrl27wsLCIs8xDx8+xI8//gh/f3/07dsXWlpaUFZWhr29Pfz9/aX5fvvtNzRp0gR6enro0KEDrl27Jt1nYWEhs2z93r17Zc5lYWGBZcuWoV27dqhatSo6d+6M8PBwAECbNm0AAHZ2dtDW1sbixYvzbVtaWhoSExNlto+Fdk1DJD+THQpJfhYHZVVVaBpWeytPXJ482jWrl1k9K4LYF0nIysqGUXXZu2OMqusiOiahWGUnJL5CVXM3qJt8iT4jVuOnxSPg8E6AVpnFxuVeW12Z9JK4tgTEvkjJub6GVWXSjapXRXRMUomc4+K1J7hxJwquI2xLpLyKpix6nCqLj6rdSUlJCA0Nxb1793DmzBmsXbtWGp3+888/mDt3Ln7//XdERUUBAG7ckJ1DoaenhzNn8u+2fdfr16+xdetWtGnTBmpqanId4+LigoCAAKSmpgIAsrKysHXrVri4uOSb//jx4zA2NkbHjh0LLDMoKAgTJ07E+vXr8fz5cwwaNAjdu3dHQoL8H7Zbt26Fv78/nj9/Di0tLXz33XcAIO0dy+1lmzNnTr7HL1myBLq6utLN1NRU7nNXCP+/fTVX7m2s4u30/PK8k6Yo3r3LN7+VgQurqrYGrp3wwMWj8+A1ZyBmzA/EybMFT4yurPK7tboc76qudErz+m4KCIZlY2O0sTYvmQIrGAZO8vuo2i2EwJIlS6ChoYEmTZrAzs4OV65cAQBs374dzs7OsLW1hZqaGjw9PaGlpSVzfHx8PDp06PDec3Ts2BF6enqoWrUq1q1bl6cXJjQ0FHp6ejLbvXs5Xe29evWCpqYmdu/eDQA4fPgwXr9+jQEDBuR7rufPn6NWrVrvrc/WrVsxcuRIdOrUCaqqqpg6dSqqVauGgwcPvve4t3311VeoW7cuNDQ04OzsLL1m8nJ3d0dCQoJ0y+2x+hgkR8fm6TnSqqGPrIwMpMbFv5XHME+ed3uqKjtD/apQVlZCdIxsj2JMbGKeXqjCUlJSQv26RmjRzAwz3LrDqa8NfvA+VKwyPyaGBrnXNl4mPefa6uZ/EMnNUF8r5/o+f/e9mwyj6lULOEp+r16lI3DfVbhU0t4mgIFTYXxU7dbR0YGm5ptJklpaWkhKyumGjYyMhLn5m28CqqqqRVpVNCgoCPHx8Xj9+jV2794NJycnhIaGSvc3a9YM8fHxMluDBg0AAMrKyhgzZox0uG7Lli0YOXJkgauoGhoa4unTp++tT0RERJ5hvjp16iAiIkLuNtWsWVP689vXTF7q6urSBc5Kc6Gz0hARHIK6DnYyafUcOyDy8g1kZ2YCAMKDQ1DXob1MnrqOHRB+7hoUiZqaClpZmePYqZsy6cdP3YJt6/olei4hBNLSM0u0zIpMTU0FrVrUwbETsr3gx06Gwq5Ng3KqVeWhpqaCVs1Ncez0XZn046fvwNamTrHL/33/NaSlZ2LkwNbFLquikqDwQZOidpZ+VIHT+5iYmODJkyfS3zMyMqRDdkWhrKyMrl27on79+jh69Kjcx40fPx4nTpzA5cuXsX///gKH6QDAwcEBUVFROHv2bIF5ateujcePH8ukPX78GLVr59z6ra2tjVevXkn3FbbN5bn6alGoamnCyKoxjKxyJoFWq1MbRlaNoWOaEyR3Wzwd/X9dKs1/ed0O6JqbwHHFbBg2rosW45xg7eKE4B83S/NcWL0V9Rzbo/23n8OgUV20//Zz1P3UFhe8fy3bxlUA0yY4YtNvQdi8PQi3/4vEtHk7EBbxAhPGdgYAuC/ahTGTNsocExIahpDQMCSnvEZsXBJCQsNw626kdP8S74M4dvImHj5+jjv3orDS9wi2/R4M50HtyrRt5W26W09s3HYCm387idt3n2LanG0Ii4jDhHE5a5G5L9iB0RN8ZY4JCX2MkNDHSE55jeexiQgJfYxbd958aUpPz5TmSc/IxNPIlwgJfYz7D2UXDVQE077ogk3+wdgcEIzb96IxzWM3wp6+xITROaMM7ov/xJjJsmthhdyIQMiNCCSnpCE2LhkhNyJw67+8n6GbA4LRv3tzGOhr5dlHiqfS3L86fPhwODk5YcyYMbC2tsaiRYuQkpJS5PKEEAgKCsKtW7fQrFmzDx/wfw0aNECHDh0wePBgNG/eHFZWVgXmrVu3LmbMmIERI0bAx8cHXbp0gbq6OoKDg7Fu3Tr89ttvGDlyJPr27YuRI0eibdu28PX1RVxcHHr16gUAaNmyJQICAjBw4EBERkZi7dq1hWqnkZERHjx4gBYtWhTquPJiYmOJsSfffPh1X5UzLyvEbzf2jXOHtnF16Jq96WmMfxwB/15foPsqd7Se5IykyBgcnvw9bu9+EwxHBF/DH8Omo6vXVHRZNBkvHoTjj6HT8PTi9bJrWAUxdEAbxL1MxqIV+xH1LAGWjWvhYMAUmJvmDGVGP4vPsy5Oy64LpD9f+fcJ/HddgLmpAR5dXQYASHmVhknf/oaIqJeooqGKxg2Msc3HFUMHtCm7hlUAQwfaIu5FMhYu24OoZ/GwbFIbhwK/gblZzlBy1LP4POtlWXd6c5PLlZBH8P/jHMxNDfH4+moAQGT0S5k8P645iB/XHETn9k1w8sC8MmhVxTG0X0vEvUzBolVHEBWTAMtGxjj42wSY19YHAETHJCLs6UuZY1o6LpP+fOV6OPz3XIF5bX08uugpTf/vQQzOXHyIIwFuZdKO8lIRVw6vqCpN4PTpp59i0aJFcHJyQmpqKiZMmJBnXSVtbW0cPnz4vZOx7ezsoKSkBIlEglq1amH58uUyT2cODQ2Ftra2zDGLFy/G5MmTpb+7uLhgzJgxee76y8/SpUvRqFEjeHh4YMiQIdDS0kKTJk2k5XXu3Bk///wzXFxcEBUVBUtLSxw+fBh6enoAAC8vLzg7O6N69epo2rQpRo8eDR8fnw+eN9eiRYswefJkuLq6YtasWZg9e7bcx5aHJ6cuYoGkUYH7941zz3vM6UvY0Grge8u9vesIbu86Uuz6VQZu47vCbXzXfPdtWZO3BzX7+ab3luc1ZyC85rz/+isKN1cHuLk65LvPzyfvbdbi5fb3lmdhVv2DeRSJ29iOcBub/+f7Fu+RedKyI3/6YJkN69WQK9/HjoGT/CRCKOitQ1RkiYmJ0NXVxWzUK/RDIUk+85/PKu8qVFoSFY3yrkKlJlLzX2WeiicxKRV6jWYhISGhROeZ5n6ee6MeqhTy8zwVWZiKByVep4qu0vQ4ERERUdGwx0l+DJyIiIgUHAMn+TFwIiIiUnAMnOTHwImIiEjBMXCSHwMnIiIiBVeUh/Z+XKsAlhxFDRiJiIiICo09TkRERAou95ErhT1GETFwIiIiUnCc4yQ/Bk5EREQKjoGT/Bg4ERERKTgGTvJj4ERERKTgGDjJj4ETERGRgmPgJD8GTkRERAqOgZP8FLXdRERERIXGHiciIiIFxx4n+TFwIiIiUnAMnOTHwImIiEjBMXCSHwMnIiIiUthHqBQWAyciIiIFxx4n+TFwIiIiUnAMnOTHwImIiEjBMXCSHwMnIiIiBScBICnkJCeJKJWqVHiKGjASERERFRp7nIiIiBSckkRAqZBdSEoQgAL2OjFwIiIiUnASSRGG6gAGTkRERKR4JCj8Ok6Kuu4TAyciIiIFl9PjVLjuIwZOREREpJCKPFSngBg4ERERKTgGTvJj4ERERKTginxXnQJi4ERERKTgODlcflwAk4iIiEhO7HEiIiJSdEWY46SoGDgREREpOE4Olx8DJyIiIgUnkYgirOPEyeFERESkgJQkOVuhjimdqlR4DJyIiIgUHIfq5MfAiYiISMFJIAo99MahOqJCmv1wGnSqVinvalRKC6svLe8qVFrzI78q7ypUbhmvyrsGlVPG61Itnj1O8lPUIUoiIiIqYz4+PqhTpw40NDTQqlUrBAUFvTf/9u3bYWVlBU1NTRgbG2PcuHGIi4sro9rmj4ETERGRgsvtcSrsVhiBgYGYOnUq5s6di2vXrqFjx47o2bMnwsLC8s1/5swZjB49Gi4uLrh58yZ27tyJS5cuwdXVtQRaXHQMnIiIiBRc7rPqCrsBQGJiosyWlpaW7zlWrlwJFxcXuLq6okmTJvD29oapqSl8fX3zzX/+/HlYWFhg8uTJqFOnDjp06IAvv/wSly9fLrXrIA8GTkRERAquOD1Opqam0NXVlW5LlizJU356ejquXLkCR0dHmXRHR0ecO3cu3zrZ2dkhIiIChw4dghACz549wx9//IHevXuXePsLg5PDiYiIFFxxHvIbHh4OHR0dabq6unqevLGxscjKyoKRkZFMupGREaKjo/Mt387ODtu3b8fQoUPx+vVrZGZm4rPPPsPPP/9cyJqWLPY4ERERKbjclcMLuwGAjo6OzJZf4PTmPLLhmRAiT1quW7duYfLkyZg/fz6uXLmCv/76C48ePcKECRNKruFFwB4nIiIiBVfayxEYGhpCWVk5T+9STExMnl6oXEuWLEH79u3xzTffAACaN28OLS0tdOzYEV5eXjA2Ni5chUsIe5yIiIgUnBLePHZF7q0Q5aupqaFVq1Y4duyYTPqxY8dgZ2eX7zGvXr2CkpLsWZSVlQHk9FSVF/Y4ERERKbgiPeS3kPmnT5+OUaNGwcbGBra2ttiwYQPCwsKkQ2/u7u54+vQptm7dCgDo27cvPv/8c/j6+qJ79+6IiorC1KlT0aZNG5iYmBTq3CWJgRMRERGVuqFDhyIuLg4LFy5EVFQULC0tcejQIZibmwMAoqKiZNZ0Gjt2LJKSkrBmzRrMmDEDenp66Nq1K5YuLd8nK0hEefZ30UcpMTERurq6iH+4ho9cKSV85Erp4SNXShkfuVIqEpNeQ8/SEwkJCTJ3sBW73P9/nl/Vr42qSoWbvZOUnY2WLyJKvE4VHXuciIiIFFyRJocr6MPqGDgREREpuLKY41RZMHAiIiJScLl3yhX2GEXEwImIiEjBcahOfgyciIiIFBwDJ/kxcCIiIlJwEghIUMg5ToXMX1lw5XAiIiIiObHHiYiISMFxqE5+DJyIiIgUnERJAkkhb5OTFOoxv5UHAyciIiIFJ1HK2Qp1TOlUpcJj4ERERKTgOFQnPwZOREREiq4oK2AqaJ8TAyciIiIFx6E6+TFwIiIiUnASiQSSQo69FTZ/ZcHAiYiISMFJJEXocVLM9S+5ACYRERGRvNjjREREpOh4W53cGDgREREpuCJNDlfQoToGTkRERAquSCuHC/Y4ERERkQLiSJ38GDgREREpuiIM1YFDdURERKSQirJyOIfqiIiISBFxqE5+XMeJiIiISE7scSIiIlJwvKtOfgyciIiIFBzXcZIfAyciIiIFx4f8yo+BExERkaKToPCznrNLoyIVHwMnIiIiBce76uTHu+roo+Oz+R/UbTULVWp/CZtuCxEU/F+BeaOi4+H85QY0bjcHyjVcMXVuQJ48uw9cQetPF6Java+gbT4R1vae2Pb7udJsQoVk1tEGw/70xfSnQfAQd9GoX7cPHmPeqTU+v7wLc1OvY/KD42j15bA8eZoMdITbzYOY+zoUbjcPonH/T0uj+h8FH78g1G3riSp1psOm+zIEXXhQYN6oZwlwdvsVjTt4QbnWFEydvytPni5OP0HJZHKerc+odaXZjArLZ2sw6rZfiioN58Gm988IuviowLy7D9+Ao/NG1LBeBN2mHrDr74Mjp/J+luw6FIqm3VZCo8FcNO22Env+ulGaTSg3OXOcJIXcyrvW5aPMm92iRQv4+fmV9WkrvaZNm+LAgQPlXY1SF7jnIqbN24E5U3vj6j8e6NCuAXoN80ZYRFy++dPSM2FooI050/rAqmntfPPoV9PCnGl9cO7wHPx7cgHGDm+P8ZO34Mg/lfMDsiBqWpp49u9dHPpqoVz59SxqY8ShDQgLuoL11v0RtHgdev40F00GOkrz1G7XAoMCV+H6tn1YZ9UP17ftw6DfvVGrTfPSakaFFbjvKqZ57MacyY64evRbdGhbD72cfREW8SLf/NL37hRHWH1ikm+eXRtdEBniJd1CT7hDWVkJg/pYl2ZTKqTA/f9i2sIDmPNVF1w9OBkd2lig15gtCHsan2/+0xcf4dOODXDQbywuH/ga9nZ18ZnLr7h246k0T/CVJxj2VQBGDrRGyOEpGDnQGkMn+ePCtbAyalXZyZ0cXthNERW62fb29vD29i6FquRPIpFAU1MT2tra0NPTg729PUJCQqT7/fz8oKysDG1tbZlt165dWLp0KSwtLfMtt3379vDw8PjgOY2MjDBs2DA8e/asNJpXJPm9Bjdv3kSfPn3Kp0JlaNW6oxjv3BGuozqhSUMTeH8/HKa19OG75WS++S3MDLF68QiMHmoHXR3NfPPYt2+MAb1boklDE9SrUwNTvnRA809q48yFe6XYkorn/l+nceI7b9zZc0yu/DYThiEhLApHpi1G7J2HuLbpD1zbvBu2M8dL87SdOgYPjp3DmR82IO7uQ5z5YQMe/X0ebaeOKa1mVFirNpzA+OHt4OpshyYNasJ7oRNMTarBd+uZfPNbmBpg9SInjB7cBro6VfLNo19NCzVr6Ei3Y6fvQLOKKgb3bVGKLamYVm08g/FDbeA6vA2aNKgBb4++MDXWhe9v5/PN7+3RF99O6IzWVqZoUMcQi7/tgQYWBtj/921pntWbz8KhQ324T+qCxvVrwH1SF3RrXx+rN58tq2aVndyxusJuCuijiBfPnTuH5ORkPH/+HO3bt8fgwYNl9jdr1gzJyckym5OTE8aOHYu7d+/i4sWLMvnv3r2L8+fPY9y4cR88Z2hoKKKiojBt2rQ8eYQQyMrKKplGyqGsz1fRpKdn4sq/T+Bo31Qm3cH+EwRful8i5xBC4O/Tt3D3QTQ62TYskTIrq9q2LfDwqOw/kAdHgmBiYwkllZzpk6a2LfDw6Jk8eUztFKtHJD09E1euh8Oxc2OZdIfOjRF8ueDhpMLaHHAeQ/u1gpameomV+TFIT8/EldCncOzYQCbdoVMDBF95IlcZ2dnZSEpJg77umy9YwVefwKGTbJmOnRrgnJxlUuVUrMDp5MmT0NPTw8aNG2FqagoDAwN8++23MnnWrFkj3Td37txiVVZVVRXOzs64f/8+0tPTP5jfyMgIvXv3xpYtW2TSt2zZgq5du8LCwuKDZdSoUQODBw9GaGgoAMDCwgJLlixBu3btoKmpiVu3buH+/fvo3r079PX1Ua9ePZneID8/P7Ro0QJz5syBgYEBzMzM4OPjI90vhMCKFStQr1496Ovro0ePHnj48KF0/7vnGzJkCIKCgjBr1ixoa2ujZ8+e0nx79+6VHvfbb7+hSZMm0NPTQ4cOHXDt2jXpPnt7e7i7u6N79+7Q1tZGy5Ytpe3LT1paGhITE2W28hD7IglZWdkwqq4jk25UXRfRMQnFKjsh8RWqmrtB3eRL9BmxGj8tHgGHdwI0kqVd0xDJz2Jl0pKfxUFZVRWahtXeyhOXJ492zeplVs+KIPZFSs5717CqTLpR9aqIjkkqkXNcvPYEN+5EwXWEbYmU9zGJffkq/+trWBXRz+W7vis2BCHlVQaG9HkzjBz9PLlYZX5MOFQnv2I3OykpCaGhobh37x7OnDmDtWvX4uTJkwCAf/75B3PnzsXvv/+OqKgoAMCNG7LzRvT09HDmTP5d1e96/fo1tm7dijZt2kBNTU2uY1xcXBAQEIDU1FQAQFZWFrZu3QoXFxe5jo+Ojsbvv/+Oli1bStP8/Pzw66+/Ijk5GY0aNUKfPn1gZWWFyMhI7NmzB8uWLYO/v780/40bNyCRSBAVFYXAwEDMnj0bp0+fBgBs27YNK1euxN69exEZGYmmTZuiT58+yMzMzPd8AQEB6NixI5YuXYrk5GQcPnw4T52DgoIwceJErF+/Hs+fP8egQYPQvXt3JCS8CS62bt2KH374AfHx8bCxscHXX39d4DVYsmQJdHV1pZupqalc1660vNs7LIQo9noiVbU1cO2EBy4enQevOQMxY34gTp69U6wyFYKQXQEv93UQb6fnl0co5sp5775Pc967JVP2poBgWDY2Rhtr85Ip8CNU1M+GgH0hWOB9HDvWDkcNQ+0SKfNjU/iJ4YVfabyyKHbgJITAkiVLoKGhgSZNmsDOzg5XrlwBAGzfvh3Ozs6wtbWFmpoaPD09oaWlJXN8fHw8OnTo8N5zdOzYEXp6eqhatSrWrVuHxYsXy+wPDQ2Fnp6ezHbvXs78lF69ekFTUxO7d+8GABw+fBivX7/GgAEDPnjOatWqoU2bNqhXrx5WrVol3Tdx4kQ0atQIysrKuHTpEqKiouDl5QUNDQ00b94cX331lcwEeC0tLXh6ekJNTQ22trZwdnbG1q1bAeQETpMnT0azZs2goaGBxYsXIyIiQmZ48e3zyRMwbt26FSNHjkSnTp2gqqqKqVOnolq1ajh48KA0z6hRo2BtbQ0VFRWMGTNG+prlx93dHQkJCdItPDz8g3UoDYb6VaGsrIToGNker5jYxDy9UIWlpKSE+nWN0KKZGWa4dYdTXxv84H2oWGVWdsnRsXl6jrRq6CMrIwOpcfFv5THMk+fdnqrKzlBfK+e9+/zd924yjKpXLeAo+b16lY7AfVfhooC9TQBgWE3z/9dXticoJi4ZRu8EQu8K3P8vXL/dhcC1zvi0g+ywXM3q2kUq82NU2aY4bdq0Kd/0zMxMuLu7F6vsYgdOOjo60NR8MyaspaWFpKScN1pkZCTMzd98+1FVVYWxsXGhzxEUFIT4+Hi8fv0au3fvhpOTk8zQUrNmzRAfHy+zNWiQ8wegrKyMMWPGSIfrtmzZgpEjR0Jd/f1zAIKCgvDy5UuEhYVhy5Yt0NfXl+4zMzOT/hwREQETExOZgKZu3bqIiIiQ/m5iYgJVVVXp7+bm5nj69Kn0+LeHDNXV1WFiYiJz/Nvnk8e7ZQJAnTp1ZMqsWbOm9GctLS0kJycXWJ66ujp0dHRktvKgpqaCVlbmOHbqpkz68VO3YNu6fomeSwiBtPTMD2dUYBHBIajrYCeTVs+xAyIv30D2/3tMw4NDUNehvUyeuo4dEH7uGhSJmpoKWjU3xbHTd2XSj5++A1ubOsUu//f915CWnomRA1sXu6yPkZqaClo1q4VjQbJzHY8H3Ydtq4J74AL2hWDcjJ3Y/tMw9O7WOM9+25bmOP5OmcdO34Pde8r8WFW2HqcZM2bAyckJL168uWv1zp07aNOmDX7//fdilV2qI5QmJiZ48uTNJLqMjAzpkF1RKCsro2vXrqhfvz6OHj0q93Hjx4/HiRMncPnyZezfv1/uYbqCKCm9uWy1a9dGZGQkMjIypGmPHj1C7dpvbn1/d39YWBhq1aolPf7x48fSfenp6YiMjJQ5/u3z5ff7u94tEwAeP34sU+bHatoER2z6LQibtwfh9n+RmDZvB8IiXmDC2M4AAPdFuzBm0kaZY0JCwxASGobklNeIjUtCSGgYbt2NlO5f4n0Qx07exMPHz3HnXhRW+h7Btt+D4TyoXZm2rbypamnCyKoxjKxy/oFUq1MbRlaNoWOa82Wn2+Lp6P/rUmn+y+t2QNfcBI4rZsOwcV20GOcEaxcnBP+4WZrnwuqtqOfYHu2//RwGjeqi/befo+6ntrjg/WvZNq4CmPZFF2zyD8bmgGDcvheNaR67Efb0JSaMzulxd1/8J8ZM3iZzTMiNCITciEByShpi45IRciMCt/7L+xm6OSAY/bs3h4G+Vp59imKaawdsCryEzYGXcPteDKYt3I+wyHhMcG4LAHBf+hfGTAuU5g/YF4Ix03/Hj/N6o521GaJjkhAdk4SExNfSPJPHtcfRoHtY6nsSd+7HYKnvSRw/ex9TxrfPc/6PnqSIWwV17do1PHv2DM2aNcOxY8ewdu1atGzZEpaWljJ35hdFqa4cPnz4cDg5OWHMmDGwtrbGokWLkJKSUuTyhBAICgrCrVu30KxZM7mPa9CgATp06IDBgwejefPmsLKyKnId3tWmTRsYGRlh/vz58PT0xL1797BmzRosW7ZMmiclJQWLFi3CvHnzcO3aNWzfvl06kXvkyJGYN28e+vbti3r16uG7775DrVq10KZNmwLPaWRkhAcPCl44b+TIkejbty9GjhyJtm3bwtfXF3FxcejVq1eJtbu8DB3QBnEvk7FoxX5EPUuAZeNaOBgwBeamOcNB0c/i86yL07LrAunPV/59Av9dF2BuaoBHV3Neo5RXaZj07W+IiHqJKhqqaNzAGNt8XDF0QMGvQWVkYmOJsSff/OPuvmoOACDEbzf2jXOHtnF16Jq96TGOfxwB/15foPsqd7Se5IykyBgcnvw9bu9+86UmIvga/hg2HV29pqLLosl48SAcfwydhqcXr5ddwyqIof1aIu5lChatOoKomARYNjLGwd8mwLx2Tm92dEwiwp6+lDmmpeObz5Er18Phv+cKzGvr49FFT2n6fw9icObiQxwJcCuTdlRUQ/taIe7lKyz66W9ExSTBsmFNHPQbC/PaOTcqRMckIiwyXpp/g/8FZGZm46vv9uGr7/ZJ08cMaoktK4YAAOxszBHw83B8t+Io5q84hnpm+tixZgTaWhduFOBjUKSH/FbgyeF16tTB6dOnMW3aNPTo0QPKysrYunUrhg3Lu0hvYZVq4PTpp59i0aJFcHJyQmpqKiZMmJBnXSVtbW0cPnwYHTt2LLAcOzs7KCkpQSKRoFatWli+fDkcHd8sshcaGgptbdkx58WLF2Py5MnS311cXDBmzJg8d/0Vl6qqKg4cOICvvvoKNWvWRLVq1TB9+nSMGDFCmsfS0hKZmZkwNjaGpqYmvv/+e3Tp0gUAMHr0aDx79gx9+vTBy5cv0aZNG+zfvx8qKgW/NFOnTsXYsWOld8y9u/Bl586d8fPPP8PFxQVRUVGwtLTE4cOHoaenV6JtLy9u47vCbXzXfPdtWZO3NzH7ef5j3bm85gyE15yBJVK3j9mTUxexQNKowP37xuWdF/Dk9CVsaPX+a3d71xHc3nWk2PWrDNzGdoTb2Pw/67Z4j8yTlh350wfLbFivhlz5FIHbaFu4jc5/nlduMJTrROCXcpU5qHczDOot/xf1j5VEUviht4o+Sf7AgQMICAiAnZ0d7t69i19++QWdOnWCiUn+C8rKSyKEgt7eUkb8/Pzg7e1d7K7BiiQxMRG6urqIf7gGOlXzX5iPimdh9aUfzkRFMj/yq/KuQuWW8aq8a1ApJSa9hp6lJxISEkp0nmnu53mcY0PoqCoX7tiMLBgc/a/E61QSvvzyS/z666/w8vLCjBkz8OzZM4wfPx4XLlyAr68vhgwZ8uFCCsCH/BIREVGlcvbsWVy4cEE6NadmzZo4dOgQ1q5di/HjxzNwIiIioqIryl1yFfmuuitXruR79/ykSZPw6afFe9B4BZ7aVTmMHTu2Ug3TERFRJaRUxK2CUldXx4MHDzBv3jwMHz4cMTExAIC//vpLZoHpoqjAzSYiIqIyoSQp2lZBnTp1Cs2aNcOFCxewe/du6VqF169fh4eHR7HKZuBERESk6CpZj9Ps2bPh5eWFY8eOySxQ3aVLFwQHBxer7ArcbCIiIioTZdTj5OPjgzp16kBDQwOtWrVCUFDQe/OnpaVh7ty5MDc3h7q6OurVq4fNmze/9xggZ5mi/B6tVr16dcTFxeVzhPw4OZyIiEjRFaUHqZD5AwMDMXXqVPj4+KB9+/ZYv349evbsiVu3bhX4aLEhQ4bg2bNn2LRpE+rXr4+YmBi55ijp6ekhKioKderIPtLo2rVr0id3FBUDJyIiIkVXlB6kQuZfuXIlXFxc4OrqCgDw9vbGkSNH4OvriyVLluTJ/9dff+HUqVN4+PCh9Hmx7z6HtSAjRozArFmzsHPnTkgkEmRnZ+Ps2bOYOXMmRo8eXah6v4tDdURERFRkiYmJMltaWlqePOnp6bhy5YrMUz8AwNHREefOncu33D///BM2NjZYtmwZatWqhYYNG2LmzJlITU39YJ2+//57mJmZoVatWkhOTsYnn3yCTp06wc7ODvPmzStaQ/+PPU5ERESKrhg9TqampjLJHh4e8PT0lEmLjY1FVlYWjIyMZNKNjIwQHR2db/EPHz7EmTNnoKGhgT179iA2NhZubm548eLFB+c5qaqqYvv27Vi4cCGuXbuG7OxsWFtbo0GDBoVrYz4YOBERESk6CQo/BvX/OCs8PFzmkSv5LTwpPeSd59sJIQp85l12djYkEgm2b98OXV1dADnDfYMGDcLatWtRpcqHH/lVr1491KtX74P5CoOBExERkaIrRo+Tjo7OB59VZ2hoCGVl5Ty9SzExMXl6oXIZGxujVq1a0qAJAJo0aQIhBCIiIvL0Hk2fPl3uqq9cuVLuvO9i4ERERKToSvmuOjU1NbRq1QrHjh2TWSbg2LFj6NevX77HtG/fHjt37kRycjK0tbUBAP/99x+UlJRQu3btPPmvXbsm8/uVK1eQlZWFRo0aSY9VVlZGq1at5K94Phg4ERERKboyuKtu+vTpGDVqFGxsbGBra4sNGzYgLCwMEyZMAAC4u7vj6dOn2Lp1K4CcO+MWLVqEcePGYcGCBYiNjcU333yD8ePH5ztMd+LECenPK1euRNWqVfHrr7+iWrVqAICXL19i3Lhx6NixY+Ha+Q4GTkRERIpOAumcpUIdUwhDhw5FXFwcFi5ciKioKFhaWuLQoUMwNzcHAERFRSEsLEyaX1tbG8eOHcPXX38NGxsbGBgYYMiQIfDy8vrguVasWIGjR49KgyYAqFatGry8vODo6IgZM2YUrvJvYeBERESk6MqgxwkA3Nzc4Obmlu8+Pz+/PGmNGzfGsWPHCn2exMREPHv2DE2bNpVJj4mJQVJSUqHLexvXcSIiIlJ0lewhvwMGDMC4cePwxx9/ICIiAhEREfjjjz/g4uKCgQMHFqts9jgRERFRpbJu3TrMnDkTI0eOREZGBgBARUUFLi4uWL58ebHKZuBERESk6MrgWXVlSVNTEz4+Pli+fDkePHgAIQTq168PLS2tYpfNwImIiEjRldEcp7KmpaWF5s2bl2iZDJyIiIgUnEQCSArZg1TAgt8VQkpKCn744Qf8/fffiImJQXZ2tsz+hw8fFrlsBk5ERESKrpL1OLm6uuLUqVMYNWoUjI2NC3ysS1EwcCIiIlJ0lWyO0+HDh3Hw4EG0b9++xMtm4ERERKToKlmPU7Vq1aCvr18qZVfgeJGIiIjKRCVbx2nRokWYP38+Xr16VeJls8eJiIiIKpUVK1bgwYMHMDIygoWFBVRVVWX2X716tchlM3AiIiJSdJVsjlP//v1LrWwGTkRERIpOCUWY41QqNSkRHh4epVY2AyciIiJFV8l6nEoTAyciIiJFV0nuqqtWrZpcaza9ePGiyOdg4ERERKToJCh8D1LFi5vg7e1d6udg4ERERKToKkmP05gxY0r9HAyciIiIFB3nOMmNgRMREZGiqyQ9TmVBQeNFIiIiosJjjxMREZGiY4+T3Bg4ERERKTrOcZIbAycqMomyOiQqGuVdjUppfuRX5V2FSmuhyZryrkKlNv/+0PKuQuWUkVa65VeCHqfp06fLnXflypVFPg8DJyIiIkVXCXqcrl27JvP7lStXkJWVhUaNGgEA/vvvPygrK6NVq1bFOg8DJyIiIkUnkeRshT2mAjlx4oT055UrV6Jq1ar49ddfUa1aNQDAy5cvMW7cOHTs2LFY56lg8SIRERGVOUkRtwpqxYoVWLJkiTRoAnIex+Ll5YUVK1YUq2wGTkRERIout8epsFsFlZiYiGfPnuVJj4mJQVJSUrHKZuBERERElaa3CQAGDBiAcePG4Y8//kBERAQiIiLwxx9/wMXFBQMHDixW2ZzjRERERJXKunXrMHPmTIwcORIZGRkAABUVFbi4uGD58uXFKpuBExERkaKrBJPDc2VlZeHSpUvw8vLC8uXL8eDBAwghUL9+fWhpaRW7fAZOREREiq4SLEeQS1lZGd27d8ft27dRp04dNG/evETLr6DNJiIiojJTySaHN2vWDA8fPiyVshk4ERERKbpKthzB999/j5kzZ+LAgQOIiopCYmKizFYcHKojIiJSdJVojhMA9OjRAwDw2WefQfJWPYUQkEgkyMrKKnLZDJyIiIgUXVF6kCpu3CSzinhJY+BERESk6CpZj1Pnzp1LrWwGTkRERFQpvXr1CmFhYUhPT5dJL86ddgyciIiIFF0lWo4AAJ4/f45x48bh8OHD+e4vzhynCtxsIiIiKhOVbDmCqVOn4uXLlzh//jyqVKmCv/76C7/++isaNGiAP//8s1hls8eJiIhI0VWyyeH//PMP9u3bh9atW0NJSQnm5uZwcHCAjo4OlixZgt69exe5bPY4ERERKbpK1uOUkpKCGjVqAAD09fXx/PlzADkLY169erVYZTNwIiIiUnCVLG5Co0aNcPfuXQBAixYtsH79ejx9+hTr1q2DsbFxscrmUB0REZGiq2TLEUydOhVRUVEAAA8PD3Tv3h3bt2+Hmpoa/Pz8ilU2AyciIiJFV8nmODk7O0t/tra2xuPHj3Hnzh2YmZnB0NCwWGUzcCIiIlJ0EgmgVHl6nN6lqamJli1blkhZnONERERElcqgQYPwww8/5Elfvnw5Bg8eXKyyGTgREREpOkkRtwrq1KlT+S450KNHD5w+fbpYZXOojoiISNFVssnhycnJUFNTy5OuqqqKxMTEYpXNHiciIiJFV0Y9Tj4+PqhTpw40NDTQqlUrBAUFyXXc2bNnoaKighYtWsiV39LSEoGBgXnSd+zYgU8++aQwVc6DPU5ERESKrgx6nAIDAzF16lT4+Pigffv2WL9+PXr27Ilbt27BzMyswOMSEhIwevRodOvWDc+ePZPrXN999x2cnJzw4MEDdO3aFQDw999/IyAgADt37ixUvd/FHiciIiJFVwY9TitXroSLiwtcXV3RpEkTeHt7w9TUFL6+vu897ssvv8SIESNga2sr97k+++wz7N27F/fv34ebmxtmzJiBiIgIHD9+HP379y9cxd/BHiciIiJFp4TCL0fw/66Xd+cMqaurQ11dXSYtPT0dV65cwezZs2XSHR0dce7cuQJPsWXLFjx48AC//fYbvLy8ClW93r17F+uZdAVhjxMREZGiK0aPk6mpKXR1daXbkiVL8hQfGxuLrKwsGBkZyaQbGRkhOjo63yrdu3cPs2fPxvbt26GiUrh+nkuXLuHChQt50i9cuIDLly8Xqqx3MXAiIiKiIgsPD0dCQoJ0c3d3LzCv5J15UUKIPGkAkJWVhREjRmDBggVo2LBhoes0adIkhIeH50l/+vQpJk2aVOjy3sahOiIiIkVXjMnhOjo60NHReW9WQ0NDKCsr5+ldiomJydMLBQBJSUm4fPkyrl27hq+++goAkJ2dDSEEVFRUcPToUemk7/zcunUr35XCra2tcevWrQ827X3Y40RERKToSnlyuJqaGlq1aoVjx47JpB87dgx2dnZ58uvo6CA0NBQhISHSbcKECWjUqBFCQkLQtm3b955PXV093zvwoqKiCj3s9y72OBERESm6MliOYPr06Rg1ahRsbGxga2uLDRs2ICwsDBMmTAAAuLu74+nTp9i6dSuUlJRgaWkpc3yNGjWgoaGRJz0/Dg4OcHd3x759+6CrqwsAiI+Px5w5c+Dg4FCoer+LgRMREZGiK8qCloXMP3ToUMTFxWHhwoWIioqCpaUlDh06BHNzcwA5vUFhYWGFrET+VqxYgU6dOsHc3BzW1tYAgJCQEBgZGWHbtm3FKlsihBAlUUlSHImJidDV1UXCk1+go6NZ3tWplERqXHlXodJaaLKmvKtQqc2/P7S8q1ApJSalQc96GRISEj44n6hQ5f7/8zx+R3foaKoW7thXGdAbdqTE61RSUlJSsH37dvz777+oUqUKmjdvjuHDh0NVtXDtfBd7nIiIiBRdJXtWHQBoaWnhiy++KPFyGTgREREpukoYOAE5d9eFhYUhPT1dJv2zzz4rcpkMnIiIiBRdJQucHj58iAEDBiA0NBQSiQS5s5Jy14zKysoqctlcjoCIiIgqlSlTpqBOnTp49uwZNDU1cfPmTZw+fRo2NjY4efJkscpm4EQfHZ+Nx1DHaio0ao5FK/u5CDp3p8C8UdEvMcJ1DRq1ngkl/ZGY6p73boqbtyPgNNobFs2nQFLNGd6+h0uz+hWej18Q6rb1RJU602HTfRmCLjwoMG/UswQ4u/2Kxh28oFxrCqbO35UnTxenn6BkMjnP1mfUutJsRoVj1tEGw/70xfSnQfAQd9GoX7cPHmPeqTU+v7wLc1OvY/KD42j15bA8eZoMdITbzYOY+zoUbjcPonH/T0uj+h8Fn98uo679z6jyyWLY9PsFQZcKvkNr95HbcBzzG2q0XgFdq6WwG7QZR04/yJOndf+NqGa9DNrNfoB13w3Ytud6aTejfEiUirZVUMHBwVi4cCGqV68OJSUlKCkpoUOHDliyZAkmT55crLLLtNUtWrSAn59fWZ5SIWlrayM0NLS8q1EqAncHY+qcbZg7ox+unfoeHW0bo+eQZQgLj803f1p6Jqob6mDujH6wsjTLN8+r1DTUNa+BHzyGoaaRXinWvuIL3HcV0zx2Y85kR1w9+i06tK2HXs6+CIt4kW/+tPRMGBpoY84UR1h9YpJvnl0bXRAZ4iXdQk+4Q1lZCYP6WJdmUyocNS1NPPv3Lg59tVCu/HoWtTHi0AaEBV3Beuv+CFq8Dj1/mosmAx2leWq3a4FBgatwfds+rLPqh+vb9mHQ796o1aZ5aTWjwgo8eBPTvj+CORM74Oqfn6NDazP0cvFHWGRCvvlPXwrDp+3r4uCm4bi8zxX27Szw2Zc7cO1mlDSPvm4VzHHrgHM7x+HfA19grJMVxs/+M0+AVSlIJDkP+S3MVoGH6rKysqCtrQ0gZ9XyyMhIAIC5uTnu3r1brLILFTjZ29vD29u7WCcsDIlEAk1NTWhra0NPTw/29vYICQmR7vfz84OysjK0tbVltl27dmHp0qUFLpLVvn17eHh4lFEr8kpNTcW8efPQoEEDaGlpoXbt2hg0aBCuXLlSIuUnJyejWbNmAABPT0/079+/RMqtCFb6HIbLSHu4ju6CJo1qwXvJKJjWMoDv5uP55rcwq47VP4zG6GEdoVvA0gmtW9bD8kUjMMzJFupqij3tb9WGExg/vB1cne3QpEFNeC90gqlJNfhuPZNvfgtTA6xe5ITRg9tAV6dKvnn0q2mhZg0d6Xbs9B1oVlHF4L4tSrElFc/9v07jxHfeuLPn2IczA7CZMAwJYVE4Mm0xYu88xLVNf+Da5t2wnTlemqft1DF4cOwczvywAXF3H+LMDxvw6O/zaDt1TGk1o8Jatfk8xg+2hutQazSpXx3e87rD1FgHvtvzf6Cr97zu+PYLO7RuboIGFgZYPLMrGpjrY/8/96R57NtZYIBjYzSpXx31zPUxZWxbNG9khDNXSmatoQold45TYbcKytLSEtev5/QOtm3bFsuWLcPZs2excOFC1K1bt1hlV9x+tv87d+4ckpOT8fz5c7Rv3x6DBw+W2d+sWTMkJyfLbE5OThg7dizu3r2LixcvyuS/e/cuzp8/j3HjxpV63TMzM/OkZWRkwNHRESdPnkRgYCDi4+Nx9+5dDBw4EHv27Cn1On3M0tMzcSXkERy7NpNJd+zSDOcu3ivgKJJXenomrlwPh2PnxjLpDp0bI/jyoxI7z+aA8xjarxW0NNVLrMzKqLZtCzw8elYm7cGRIJjYWELp/4+MMLVtgYdHz+TJY2qnWL156elZuHIjCo4dZP8hOnSoh+CrEXKVkZ0tkJSSDn3d/L8ACCHw97lHuPsoDp1amxe7zhVOJRuqmzdvHrKzswEAixYtwpMnT9CxY0ccOnQIq1evLlbZRW71yZMnoaenh40bN8LU1BQGBgb49ttvZfKsWbNGum/u3LnFqqiqqiqcnZ1x//79PLcV5sfIyAi9e/fGli1bZNK3bNmCrl27wsLCIs8xjx8/hkQiwS+//AILCwsYGBjAzc1N5nxXr15Fly5doK+vj/r16+OXX36R7vP09ESfPn0wceJE6OvrY9asWXnO4e/vj9u3b+PAgQNo2bIlVFVVoaWlhREjRsDLywsAcPToUdjY2EBXVxfGxsZwc3NDamqqtAwLCwt8//33aNmyJXR0dNC9e3dpNySQ01MXEhKCvXv3YvHixThw4IC0N06e8t+VlpaGxMREma08xMYlISsrG0bVdWXSjarrIjom/+54kl/si5Sc62tYVSbdqHpVRMcklcg5Ll57ght3ouA6wrZEyqvMtGsaIvmZ7BB08rM4KKuqQtOw2lt54vLk0a5ZvczqWRHEvnyFrCwBI0MtmXQjAy1ExybLVcaKTcFISc3AkN6fyKQnJL1G1eY/QL3JYvRxDcBP83vAoUPxeiwqpErW49S9e3cMHDgQAFCvXj3cunULsbGxiImJQbduH55f+D7FCheTkpIQGhqKe/fu4cyZM1i7dq10tvo///yDuXPn4vfff0dUVM6Y8Y0bN2SO19PTw5kz+Q8BvOv169fYunUr2rRpAzU1NbmOcXFxQUBAgDQoyMrKwtatW+Hi4vLe4/bs2YOQkBCEhobi3LlzWLJkCQAgOjoaDg4OmDhxIp4/f469e/fCw8MDf//9t/TYv/76C23btkVMTAwWLVqUp+wjR46gZ8+e0NPTK/D8VapUwS+//IIXL17g7NmzOHHiBFauXCmTZ+PGjfD390d0dDRq1qwJZ2fnPOX0798fc+bMQZ8+faS9cfKW/7YlS5ZAV1dXupmamr73+pU2yTt/rEKIivz3+9Epzeu7KSAYlo2N0ca6En5jLw3vPNgh97WReeBDfnkU9IEQkneeASIg8ryf8xOw/wYW/HQaO1YPRA0D2eCrqpY6rv35BS7udoHX9C6YsfgoTp5/XJLVrhgKO78pd6tgBg4cWODm6uoKJycnDBkyBJMnT8b+/fuLdI5iBU5CCCxZsgQaGhpo0qQJ7OzspPN0tm/fDmdnZ9ja2kJNTQ2enp7Q0pJ9Q8bHx6NDhw7vPUfHjh2hp6eHqlWrYt26dVi8eLHM/tDQUOjp6cls9+7lDNv06tULmpqa2L17NwDg8OHDeP36NQYMGPDec3p6ekJPTw8mJiZwd3eXPtdm27Zt6NSpE4YMGQJlZWVYWlpi3Lhx8Pf3lx5raWmJsWPHQkVFBZqaeefUPH/+HLVq1fpgm62traGsrIy6deviyy+/zHP75MSJE9G4cWNoampi2bJlOHnyJCIi5OuSlqf8t7m7uyMhIUG6hYeHy3WekmZoUBXKykqIjomXSY+JTczTC0WFZ6ivlXN9n8v2KMbEJsOoetUCjpLfq1fpCNx3FS7sbZJLcnRsnp4jrRr6yMrIQGpc/Ft5DPPkebenqrIzrKYJZWVJnt6lmLhXMHonEHpX4MGbcHXfj8CfnPBp+7w9SUpKEtS30EeLT2pihqstnHo0wQ/rzuZT0keukgzVvf0lv6CtSpUquHfvHoYOHYr58+cX+hzFmgmro6MjExxoaWkhKSmnSz8yMhL29vbSfaqqqjA2Ni70OYKCgtCiRQtkZWXh1KlTGDhwIIKCgqSTn5s1ayYzYfxtysrKGDNmDLZs2QJnZ2ds2bIFI0eOhLr6++dW5D5wMPfnp0+fAsgZyjt06JBMb1FWVhY6duwo/d3MLP87t3IZGhpKyyvIpUuX4O7ujtDQUKSmpiIzMxONGjUqsI5GRkZQV1fH06dPUbt27feWLW/5b1NXV//gNSsLamoqaNWiDo6duIEBfVpL04+dDEW/nq3KsWaVg5qaClo1N8Wx03cxoKeVNP346Tv4rHuz9xwpn9/3X0NaeiZGDmz94cyEiOAQNOzbRSatnmMHRF6+gez/z58MDw5BXYf2OO/9qzRPXccOCD93rUzrWt7U1JTRytIYx848xADHN3P0jp95iM8+bVjgcQH7b8Bl9n74ew9A7y4N5DqXEEBaetEXT6ywKskCmO9Oz3mfgwcPYuLEiVi4UL47XXOVWrhoYmKCJ0+eSH/PyMiQDtkVhbKyMrp27Yr69evj6NGjch83fvx4nDhxApcvX8b+/fs/OEwHQKbeYWFh0h4iU1NTDBgwAPHx8dItKSkJhw4dkuZXUnr/Je3evTv++usvJCQUPCdn+PDh6NKlCx4+fIjExEQsXrwY7z6L+e06xsTEIC0tLd+erPzqI0/5FdV0t57YuO0ENv92ErfvPsW0OdsQFhGHCeNyxqzdF+zA6Am+MseEhD5GSOhjJKe8xvPYRISEPsatO29659LTM6V50jMy8TTyJUJCH+P+w+gybVtFMO2LLtjkH4zNAcG4fS8a0zx2I+zpS0wYndMz7L74T4yZLLsWVsiNCITciEByShpi45IRciMCt/7L+7e+OSAY/bs3h4H++3sAKitVLU0YWTWGkVXOP/ZqdWrDyKoxdExzvlB2Wzwd/X9dKs1/ed0O6JqbwHHFbBg2rosW45xg7eKE4B83S/NcWL0V9Rzbo/23n8OgUV20//Zz1P3UFhfeCqQUxbTx7bBp5zVs3hmC2/efY5rXUYRFJWDCiJwvVe7L/8aYmXul+QP238CYb/bhR3cHtGtRG9HPkxH9PBkJSa+leZb4nsGxMw/xMOwl7jyIxcpN57Ft73U49yv+Fwkqf+3bt4eNjU2hjyu1e6+HDx8OJycnjBkzBtbW1li0aBFSUlKKXJ4QAkFBQbh165a0t0keDRo0QIcOHTB48GA0b94cVlZWHzxm4cKF+O233/Dq1SssWbJEOn9o1KhRWLlyJXbt2iV9zs3NmzeRkZGB1q3l+xbt7OyMTZs2oW/fvli9ejWaNWuGjIwM7N+/H6GhoVi0aBESExOhp6cHLS0t3L59G76+vqhSRfZOj/Xr16Nfv34wMzPDrFmz0KlTp3x7m4yMjPDkyRNkZWVBWVkZAOQqv6IaOtAWcS+SsXDZHkQ9i4dlk9o4FPgNzM1yhjSinsUjLEJ2sqx1pzc3JlwJeQT/P87B3NQQj6/n3FkRGf1SJs+Paw7ixzUH0bl9E5w8MK8MWlVxDO3XEnEvU7Bo1RFExSTAspExDv42Aea19QEA0TGJCHv6UuaYlo7LpD9fuR4O/z1XYF5bH48uekrT/3sQgzMXH+JIgFuZtKMiMrGxxNiTb4LO7qvmAABC/HZj3zh3aBtXh67Zm175+McR8O/1BbqvckfrSc5IiozB4cnf4/buN18cI4Kv4Y9h09HVayq6LJqMFw/C8cfQaXh6sZIu0vgeQ3s3RdzLVCxacxpRMcmwbFgdBzcOh3ktPQBA9PNkhEW+GYbeEHAVmZnZ+MrzML7yfLPo7ZiBzbFlWT8AQEpqBiZ5HEZEdCKqaKigcV1DbFvRH0N7Ny3TtpWJStLjVBh6enrSqTyFUWqB06effopFixbByckJqampmDBhQp51lbS1tXH48GGZoa532dnZQUlJCRKJBLVq1cLy5cvh6PhmAbjQ0FDp3WK5Fi9eLLMyqIuLC8aMGZPnrr+C9OvXDy1atEBiYiKGDBmCOXNyPuBq1aqFI0eOYNasWfjyyy+RnZ2NJk2aFKqbT1VVFUeOHMH333+PwYMHIzIyEvr6+mjXrp30POvXr8f06dMxa9YstGrVCsOGDcO+fftkyhk/fjyGDx+O+/fvo127dti+fXu+5xs8eDD8/f1haGgIIQTi4+PlKr8ic3N1gJurQ777/Hwm5EkTL/O/NrkszKp/MI8icRvbEW5j8/+b3OI9Mk9aduRPHyyzYb0acuWrzJ6cuogFkoKHxPeNc897zOlL2NBq4HvLvb3rCG7vOlLs+lUGbiNt4DYy/x6E3GAo1wn/0R8sz2t6F3hN7/LBfJWCRFL4OUsfeeBUVBLxsYzRlIHHjx+jTp06ePny5XvveitvFhYW8Pb2LreFLRMTE6Grq4uEJ79Ap4BFJal4RGrchzNRkSw0WVPeVajU5t8fWt5VqJQSk9KgZ70MCQkJ0NHRKbly//95Hv/XMOhoyXfHuvTYlHTo9dhR4nWq6BR7mWQiIiJSyKG6omLgREREpOiKsrxABVyOoCwwcHqLhYXFR3F32ePHj8u7CkREVJmwx0luDJyIiIgUXVFWAq+AK4eXBcXsZyMiIiIqAvY4ERERKbyiPEJFMfteGDgREREpOs5xkhsDJyIiIkXHwEluDJyIiIgUHQMnuTFwIiIiUnRKSjlbYY9RQAyciIiIFB17nOTGwImIiEjRMXCSGwMnIiIiRSeRFOGRK4oZOCnmACURERFREbDHiYiISNHxkStyY+BERESk6DjHSW4MnIiIiBSdpAiPXCn0I1oqBwZOREREio49TnJj4ERERKToGDjJjYETERGRouPK4XJj4ERERKTwJP/fCnuM4lHMcJGIiIioCNjjREREpOg4x0luDJyIiIgUXhGWI1DQQSsGTkRERAqPc5zkxcCJiIhI0XGoTm4MnIiIiBQdVw6XGwMnIiIihcehOnkxcCIiIlJ0EhRhqK5UalLhMXAiIiJSeEoo/F1yijlUp5itJiIiIioCBk5ERESKLveuusJuheTj44M6depAQ0MDrVq1QlBQUIF5d+/eDQcHB1SvXh06OjqwtbXFkSNHitPKEsHAiYiISNGVQeAUGBiIqVOnYu7cubh27Ro6duyInj17IiwsLN/8p0+fhoODAw4dOoQrV66gS5cu6Nu3L65du1YSLS4yBk5EREQKT1LETX4rV66Ei4sLXF1d0aRJE3h7e8PU1BS+vr755vf29sa3336L1q1bo0GDBli8eDEaNGiA/fv3F7GNJYOBExERkaLLXcepsBuAxMREmS0tLS1P8enp6bhy5QocHR1l0h0dHXHu3Dm5qpidnY2kpCTo6+sXv73FwMCJiIhI0RVjqM7U1BS6urrSbcmSJXmKj42NRVZWFoyMjGTSjYyMEB0dLVcVV6xYgZSUFAwZMqT47S0GLkdARESk8Iq+AGZ4eDh0dHSkqerq6gUf8c68KCFEnrT8BAQEwNPTE/v27UONGjUKWc+SxcCJiIhI0RXjkSs6OjoygVN+DA0NoaysnKd3KSYmJk8v1LsCAwPh4uKCnTt34tNPPy1cHUsBAycqMvH6BYRqanlXo3LKeFXeNai05t8fWt5VqNQW1g8s7ypUSq+RVarlSyQSuXp+3j1GXmpqamjVqhWOHTuGAQMGSNOPHTuGfv36FXhcQEAAxo8fj4CAAPTu3btQ9SstDJyIiIio1E2fPh2jRo2CjY0NbG1tsWHDBoSFhWHChAkAAHd3dzx9+hRbt24FkBM0jR49GqtXr0a7du2kvVVVqlSBrq5uubWDgRMREZHCK/2H/A4dOhRxcXFYuHAhoqKiYGlpiUOHDsHc3BwAEBUVJbOm0/r165GZmYlJkyZh0qRJ0vQxY8bAz8+vkHUtOQyciIiIFF0x5jgVhpubG9zc3PLd924wdPLkyUKXXxYYOBERESm80u9xqiwYOBERESm6ojx7rgjPqqsMGDgREREpOomkCEN1DJyIiIhIIXGoTl4MnIiIiBQdh+rkxmfVEREREcmJPU5ERESKroyWI6gMGDgREREpPM5xkhcDJyIiIkXHOU5yY+BERESk8JRQ+GnPHKojIiIiRcQeJ7kxcCIiIlJ0DJzkxsCJiIhI4XGoTl4MnIiIiBReEXqcFPSuOsUMF4mIiIiKgD1ORERECo/rOMmLgRMREZGi48rhcmPgREREpOgkKMJddaVSkwqPgRMREZHC41CdvBg4ERERKToO1cmNgRMREZHCY4+TvBg4ERERKTquHC43xexnIyIiIioC9jgREREpPD5yRV4MnIiIiBQdh+rkxsCJiIhI0fGuOrkxcCIiIlJ4vKtOXgyciIiIFB2H6uTGwImIiEjhcXK4vBg4ERERKTr2OMmNgRMREZHCY4+TvBSz1URERERFwB4nIiIiRcehOrkxcCIiIlJ0EhQhcCqVmlR4DJyIiIgUHuc4yYuBExERkaLjUJ3cGDgREREpPK4cLi/F7Gejj5qPXxDqtvVElTrTYdN9GYIuPCgwb9SzBDi7/YrGHbygXGsKps7flSdPF6efoGQyOc/WZ9S60mxGheWzNRh12y9FlYbzYNP7ZwRdfFRg3t2Hb8DReSNqWC+CblMP2PX3wZFT/+XJt+tQKJp2WwmNBnPRtNtK7PnrRmk2ocLy+e0y6tr/jCqfLIZNv18QdCmswLy7j9yG45jfUKP1CuhaLYXdoM04cvpBnjyt+29ENetl0G72A6z7bsC2PddLuxkVkllHGwz70xfTnwbBQ9xFo37dPniMeafW+PzyLsxNvY7JD46j1ZfD8uRpMtARbjcPYu7rULjdPIjG/T8tjeqXv9xn1RV2U0CK2Wr6aAXuu4ppHrsxZ7Ijrh79Fh3a1kMvZ1+ERbzIN39aeiYMDbQxZ4ojrD4xyTfPro0uiAzxkm6hJ9yhrKyEQX2sS7MpFVLg/n8xbeEBzPmqC64enIwObSzQa8wWhD2Nzzf/6YuP8GnHBjjoNxaXD3wNe7u6+MzlV1y78VSaJ/jKEwz7KgAjB1oj5PAUjBxojaGT/HHhWsFBQ2UUePAmpn1/BHMmdsDVPz9Hh9Zm6OXij7DIhHzzn74Uhk/b18XBTcNxeZ8r7NtZ4LMvd+DazShpHn3dKpjj1gHndo7Dvwe+wFgnK4yf/WeeAEsRqGlp4tm/d3Hoq4Vy5dezqI0RhzYgLOgK1lv3R9Didej501w0GegozVO7XQsMClyF69v2YZ1VP1zftg+DfvdGrTbNS6sZ5UhSxE3xSIQQorwrURGNHz8eW7Zswa1bt9CkSZPyrk6RSSQSXLt2DS1atCixMhMTE6Grq4v4u0uhU7VKiZUrj3a9V8C6WW34/jBUmvZJp+/Rr0czLJnz2XuP7eL0E6ya1oL3Qqf35vP+5QQ8lh9CZIgXtDTVS6TehZbxqlxO267fWlhbmsD3+wHStE+6rkC/7k2xZFYPucqw/HQlhvRtjvlTcr6ZD5vkj8Sk1zi0dbw0T8/Rm1FNtwr8fx5esg2QR0ZK2Z8TQDunTbBuagzfhb2kaZ9090G/TxthyTcf7h0BAMsevhjSuynmf92pwDytPvsFvbrUx6JpXYpd56JYWD+wXM77Ng9xFzv6u+Huvr8LzPPpDzPR8LOu8PnkzevR23cBjKwaYbNdTs+T045VUNfRhn+vz6V5nA9vROrLBOweMaP0GpCP18jCD3iAhIQE6OjolFi5uZ/nCbEHoaOjVchjU6Br2LvE61TRsccpH8nJyfj999+hr6+PTZs2ldp5MjMzS63syig9PRNXrofDsXNjmXSHzo0RfLng4aTC2hxwHkP7tSq/oKmcpKdn4kroUzh2bCCT7tCpAYKvPJGrjOzsbCSlpEFfV1OaFnz1CRw6yZbp2KkBzslZZmWQnp6FKzei4Nihrky6Q4d6CL4aIVcZ2dkCSSnp0NfN/8uKEAJ/n3uEu4/i0Km1ebHrXNnVtm2Bh0fPyqQ9OBIEExtLKKnkTP81tW2Bh0fP5Mljaqd4vdH0BgOnfOzYsQNaWlpYunQptm7dioyMDOm+n3/+GaampjAwMMC8efPQokUL+Pn5ybXfz88PLVq0gIeHB2rWrImhQ4dKz9e8eXPo6emhdevWOHfunLS8+Ph4DB48GHp6emjcuDF+/vlnSN66k2H79u2wtLRE1apVYWZmhu+++w65nYht2rQBANjZ2UFbWxuLFy8GADx48AB9+/ZF9erVYW5uDi8vL2RnZxd4PdLS0pCYmCizlYfYFynIysqGkWFVmXSj6lURHZNUIue4eO0JbtyJgusI2xIp72MS+/JV/tfXsCqin8t3fVdsCELKqwwM6fNmKCP6eXKxyqwMcq6tgJGh7Dd6IwMtRMcmy1XGik3BSEnNwJDen8ikJyS9RtXmP0C9yWL0cQ3AT/N7wOGdAI3y0q5piORnsTJpyc/ioKyqCk3Dam/licuTR7tm9TKrZ9nhUJ28GDjlY9OmTXB2dsawYcPw6tUr7N+/HwDw999/Y/78+di1axeioqKgpKSEmzdvSo/70H4AuHHjBlRUVBAWFoZt27bh0KFDmDlzJvz8/PDixQu4u7ujb9++iIvL+WP9+uuvkZKSgidPnuDEiRPYtm2bTHn6+vrYvXs3EhMT8eeff2LDhg3w9/cHAFy8eBEAcO7cOSQnJ2POnDlITU1Ft27d0LVrVzx9+hRBQUHYsWMHtmzZUuD1WLJkCXR1daWbqalp8S9yMUjeuQVWCFFid8VuCgiGZWNjtLFW3G/s717LnOv74QscsC8EC7yPY8fa4ahhqF0iZVY2knf+0QjIeW3338CCn05jx+qBqGEgG3xV1VLHtT+/wMXdLvCa3gUzFh/FyfOPS7Laldc7M1VyXwuZGSz55amMM1xylyMo7KaAGDi949atWzh//jzGjBkDbW1tDBgwQDpc5+/vD2dnZ7Rp0wZqamr47rvvoKX15kPsQ/sBQFdXF3PnzoWamho0NTWxdu1afPPNN2jZsiWUlJQwcOBANG7cGIcOHUJWVhYCAwOxcOFC6OrqwtjYGN98841MeT179kTDhg0hkUjQokULDB8+HCdPniywfQcOHEC1atUwbdo0qKmpwczMDFOmTJEGW/lxd3dHQkKCdAsPDy/ClS0+Q30tKCsrIfq5bI9XTGwyjKpXLeAo+b16lY7AfVfhooC9TQBgWE3z/9dXticoJi4ZRu8EQu8K3P8vXL/dhcC1zvi0g+ywXM3q2kUqszLJubaSPL1LMXGvYGTw/nklgQdvwtV9PwJ/csKn7fP2JCkpSVDfQh8tPqmJGa62cOrRBD+sO5tPSfS25OjYPD1HWjX0kZWRgdS4+LfyGObJ825PVaXAu+rkppitfo9NmzbBysoKVlZWAIAxY8bgyJEjePr0KSIjI2V6W1RVVWFsbCz9/UP7AaBWrVpQUnpz2R8/fow5c+ZAT09PuoWEhODp06eIjY1FRkaGTJlmZmYy5R05cgR2dnYwNDSErq4u1q1bh9jYgv+oHz9+jBs3bsicb8aMGYiOji7wGHV1dejo6Mhs5UFNTQWtmpvi2Om7MunHT9+BrU2dYpf/+/5rSEvPxMiBrYtd1sdITU0FrZrVwrGg+zLpx4Puw7ZVwT1wAftCMG7GTmz/aRh6d2ucZ79tS3Mcf6fMY6fvwe49ZVY2amrKaGVpjGNnHsqkHz/zELYtaxd4XMD+Gxj37Z/YvmoAendpUGC+twkBpKVnFau+iiAiOAR1Hexk0uo5dkDk5RvI/v/80/DgENR1aC+Tp65jB4Sfu1Zm9Sw7HKqTFxfAfEtGRga2bduG5ORk1KxZE0BOl21WVhb8/PxgYmIi09uSmZmJqKg3twZ/aD8AmaAJAExNTfH1119jwoQJeeqTlZUFVVVVhIeHw8jICAAQFvbmFu709HQMHDgQPj4+GDZsGNTV1TF16lQ8fvxYmufdYQBTU1O0atUK58+fl/eyVCjTvuiC0ZO3waa5KWxt6mDDb+cQ9vQlJozuAABwX/wnIqMT8OtPo6THhNzImXybnJKG2LhkhNyIgJqaMj5pKBvUbg4IRv/uzWGgX7g7SyqTaa4dMHra77BpXgu2Lc2xIeACwiLjMcG5LQDAfelfOdd3Vc78vIB9IRgz/Xd4e/RFO2sz6VyzKhqq0NXRAABMHtcenYesx1Lfk+jn8An2HbuF42fvI+iPvO/5ymza+HYYPXMvbJqZwNa6FjbsuIawqARMGNEKAOC+/G9EPkvCrz/2B5ATNI35Zh+853VHuxa1Ef08p7eqioYKdKvmXNslvmdg08wE9cyqIT0jC4dO3se2vdfhs6BXvnWozFS1NKFf/80Xy2p1asPIqjFSXyQgMTwK3RZPR9VaRtg7ZhYA4PK6HWj9lTMcV8zG1V9+R21ba1i7OGHX8Dd3y11YvRXjTv+G9t9+jjv7/kbjft1Q91NbbOkwoszbV+q4crjcGDi95c8//0RiYiJCQkKgp6cnTffx8cHmzZuxbt06DB06FGPHjoWVlRW+//57pKS8ubV5+PDhGDJkSIH78/PVV19hypQpaN26NVq2bInU1FScO3cOjRs3Ru3atTFkyBB4enrC398fr169wooVK6THpqWl4fXr1zAwMIC6ujouXLgAf39/2Nm9+RZlZGSEBw8eSJcj6NOnD9zd3eHj44Px48dDVVUV9+/fR1RUFOzt7UvkOpamof1aIu5lChatOoKomARYNjLGwd8mwLy2PgAgOiYRYU9fyhzT0nGZ9Ocr18Phv+cKzGvr49FFT2n6fw9icObiQxwJcCuTdlRUQ/taIe7lKyz66W9ExSTBsmFNHPQbC/PaOZNlo2MSERYZL82/wf8CMjOz8dV3+/DVd/uk6WMGtcSWFUMAAHY25gj4eTi+W3EU81ccQz0zfexYMwJtrWV7Tyu7ob2bIu5lKhatOY2omGRYNqyOgxuHw7yWHoCcSfRhkW+GoTcEXM25tp6H8ZXnYWn6mIHNsWVZPwBASmoGJnkcRkR0IqpoqKBxXUNsW9EfQ3s3LdO2VQQmNpYYe/LNHNDuq+YAAEL8dmPfOHdoG1eHrtmbL0vxjyPg3+sLdF/ljtaTnJEUGYPDk7/H7d1HpXkigq/hj2HT0dVrKrosmowXD8Lxx9BpeHqxMi4yymfVyYvrOL2lV69eMDIyyjNROjY2Fqampjh48CBCQ0OxfPlypKamYuLEidizZw/mzZuH4cNz1qNZvXp1gfv9/Pzg7e2NkJAQmfJ37tyJJUuW4OHDh1BXV0ebNm2wdu1amJmZ4eXLl3B1dcXx48dhbGyML774Au7u7khLSwMArFu3DgsXLkRycjLs7e1hbm6O8PBw7N27FwCwceNGeHh44NWrV5g1axZmz56NBw8e4Ntvv8XZs2fx+vVr1KtXD9988w2GDcu7am5+ynMdJ4VRTus4KYRyWsdJUVSEdZwqo1Jfxyn+b+joFG7eYWJiMnT1uhWqTj4+Pli+fDmioqLQtGlTeHt7o2PHjgXmP3XqFKZPn46bN2/CxMQE3377bb4jNGWJgVMxpKenw8DAAIcPH0aHDh0Kvb8o/P39MX/+fNy/f//DmUsJA6cywMCp9DBwKlUMnEpHZQicAgMDMWrUKPj4+KB9+/ZYv349Nm7ciFu3buWZvwsAjx49gqWlJT7//HN8+eWXOHv2LNzc3BAQEAAnp/cvZFyaFLOfrRh2796N1NRUpKSkYNasWdDX15eulyTP/sK6d+8eLl++DCEE7t27h++//x6DBw8uiaYQERH9n1IRN/mtXLkSLi4ucHV1RZMmTeDt7Q1TU1P4+vrmm3/dunUwMzODt7c3mjRpAldXV4wfPx4//vhjEdtYMjjHqZC2bduG8ePHQwgBKysr7Nu3D2pqanLvL6yUlBSMHDkS4eHh0NXVRf/+/TFv3rySaAoREREAIDHpVaEneycm5fSMv7sosrq6OtTVZZ+8kJ6ejitXrmD27Nky6Y6OjjKLPr8tODgYjo6OMmndu3fHpk2bkJGRAVVV1ULVt6QwcCqkPXv2FGt/YbVo0QJ37twp0TKJiIgAQE1NDTVr1oSpae8iHa+trZ1nUWQPDw94enrKpMXGxiIrK0t6h3guIyOjApfDiY6Ozjd/ZmYmYmNj8yz3U1YYOBERESkoDQ0NPHr0COnp6UU6Pr+nALzb2/S2/J/8UHBPV37580svSwyciIiIFJiGhgY0NDRK9RyGhoZQVlbO07sUExOTp1cpV82aNfPNr6KiAgMDg1Kr64dwcjgRERGVKjU1NbRq1QrHjh2TST927JjM2oNvs7W1zZP/6NGjsLGxKbf5TQADJyIiIioD06dPx8aNG7F582bcvn0b06ZNQ1hYmHRdJnd3d4wePVqaf8KECXjy5AmmT5+O27dvY/Pmzdi0aRNmzpxZXk0AwKE6IiIiKgNDhw5FXFwcFi5ciKioKFhaWuLQoUMwN895bmVUVJTMY8Xq1KmDQ4cOYdq0aVi7di1MTEzw008/lesaTgAXwKQi4AKYZYALYJYeLoBZqrgAZukorQUwqfA4VEdEREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJi4EREREQkJwZORERERHJSKe8K0MdHCAEASEx+Xc41qcQyeG1LTUZaedegUnuNrPKuQqWUhmwAbz5/qfwwcKJCS0pKAgCYtfIo55oQESmWpKQk6Orqlnc1FJpEMHylQsrOzkZkZCSqVq0KiURS3tX5oMTERJiamiI8PBw6OjrlXZ1Khde2dPH6lq6P6foKIZCUlAQTExMoKXGWTXlijxMVmpKSEmrXrl3e1Sg0HR2dCv/h+LHitS1dvL6l62O5vuxpqhgYthIRERHJiYETERERkZwYOFGlp66uDg8PD6irq5d3VSodXtvSxetbunh9qSg4OZyIiIhITuxxIiIiIpITAyciIiIiOTFwIiIiIpITAyciIiIiOTFwoo9WVhafiUVERGWLgRN9tJSVlZGeno5Xr16Vd1UqJSEEsrOzy7saREQVCgMn+milp6fDzMwMAQEBAICtW7fi5MmT5VupSiIrKwsSiQRKSkoyT2Pn6iUlhz2mpYvvVSotXMeJPkrZ2dlQUlKCj48PFixYAHV1ddSoUQO7d++GmZlZeVfvo5V7XXP98MMP+Pfff+Ho6Ijhw4dDQ0MDQoiP4uHOFVVWVhaUlZUBAGlpaUhISECNGjXKuVaVR24vKR+ES6WF7yz6qGRmZgJ4823ywYMHiI2NRdOmTXH58mWYmZnxm2Yx5P6zefr0KcaNG4cDBw7AxMQEK1aswNdffw2A3+SLKzdoWrx4MVq0aIH+/ftj4cKFiIqKKueaVQ5KSkpQUlLCnTt3sH79epw7dw6JiYkA+N6lksEeJ/oovN3LIYRAREQETE1N8ezZM1y7dg39+vXDpUuXYGlpyW+ahfRuL5OPjw8CAwNhbGyMHTt2AABOnz6Nbt264fz582jVqhV7nQrh5cuXqFatmvT358+fw9XVFU+fPsWaNWtw+/ZtHDx4EDVr1sSaNWvKsaYfr9z3oxACQgjMnj0bGzduRI8ePRAaGooOHTpgwYIFqFGjBt+7VGz8D0MV3tsfdOvXr0etWrUwaNAgfPPNN1BXV0ePHj3Qrl07zJ07F9nZ2fxWKafcOTbvBpr29vZISkpCdHS09Jt6x44d4ezsjM8//xwA+I/nPeLi4gDk9I6uWrUKbm5uAIB79+7h1q1b0NDQgKOjIy5duoR27dqhXbt2uHfvHvbs2YPjx4+XZ9U/Ovfv3wfw5v0okUhw584d/Pvvv7h69Sr8/f3h7++P48ePw8vLSyYvUVExcKIKKTf4yQ2aQkNDsW/fPuzfvx+7d+/Gl19+iYsXL2L+/PkAgM2bN+PQoUM4cuQIJBIJbt26hdDQ0PJsQoUTHh4u83vukJGPjw+cnZ3h5eWFe/fu4ZNPPsHo0aOhrKyMK1euAMj5Z+Ph4YFHjx5hw4YNADjskZ+QkBA0atQIz58/h4qKCkxMTPD8+XP06tULjRo1wokTJ6CpqYlBgwYBAKZNm4YOHTrA3t4eDg4O+Pnnn8u5BR+P1atXw8bGBgBw5MgRLFu2DBkZGfD394eenh4sLCxw4MABDB48GDVq1MBXX31VzjWmSkMQfQS6du0qNDU1hZeXlxBCiNTUVPHHH38IMzMzcenSJSGEEG5ubqJFixaiTZs2wsLCQly4cKE8q1yhuLm5iZkzZ4r09HRpWnJysnBychJWVlZizZo1wt7eXlhZWYmgoCCRmpoqHBwcxDfffCOio6Olx8ydO1f07NmzPJrw0YiNjZX+fPToUSGRSISxsbG4ffu2TL69e/eKrl27isePHwshhBgwYIBQV1cX3t7eZVrfj1mNGjVEo0aNhI6Ojti5c6cQQogdO3YIa2tr4ejoKExNTYWvr680/+nTp8urqlSJqJR34EZUkF9//RX37t2Dl5cX1qxZA0dHR2hoaCAzMxMaGhqws7ND586d4enpiQMHDmDt2rX4448/EBUVhUmTJin0XCfx/5663Du4vLy8ZObZAMDNmzcRHh6Oq1evQklJCZMmTcLQoUOxefNm2NjY4PPPP4ePjw9OnTqFIUOGAAA8PDygqqpaHk36KAghYGBggNOnT0NDQwN16tSBr68vjh8/jqNHj6Jx48bS12Tv3r0wNDSEubk5Tp06BTU1Nfj6+qJdu3bl3YwK6e27ETMzM3H37l1kZGTg0aNHuH37NurWrQsAqFWrFjQ0NJCcnIywsDBp/okTJ6JOnTpo1aoVNDU1y60dVAmUd+RGJIQQ2dnZMr+npaUJb29vYWxsLB4+fCiEEGLSpEnC3t5e3L17V5rv5MmTwsDAQGzbtq1M6/uxyMjIkPl948aN4s6dO0IIIf7++2/RoEEDERERId1/6tQp0ahRI/Ho0SMhhBAODg5i/PjxIi4u7r3lKrL8rsXQoUNF3bp1RVZWlhBCCE9PT+Hg4CBu3LghzRMQECBMTU1Fy5YthYGBgQgMDCyzOn9MsrKyZD4f3u4BFUKIdu3aidGjR4vXr18LIXJ6Ujdt2iQaN24s5s2bJxYuXChq1qwpBgwYINMbSFRUDJyowrhz544IDg4WmZmZQgghnjx5Ivr37y+GDh0qhBAiPj5eNGjQQCxfvlwkJycLIYR48eKFWL16tbh8+XK51bsiunz5spgwYYI06Dx37pzIzMwU1apVE15eXiIrK0v8888/wtHRUezZs0cIkRO8ZmRkiOrVq4sDBw4IIXJek+fPn5dXMyq0d4P90NBQ6VDo69evha6urlizZo0QQoiLFy+KIUOGiGnTpknzp6WlifDwcPHHH3+8t1zKcfr0aWFrays6duwopkyZIv2bv3jxopBIJOLkyZMy1+6vv/4Sq1atEl988YU4deqUNJ3Xl4qLgROVi9zg6G2DBg0SnTp1ks75yMrKEgcOHBAWFhbi2LFjQgghVq9eLZo0aSLOnDlTpvX9WOT+U/jvv/9EmzZtxOTJk4WlpaWwtbUVQgjh6+srLC0tRUhIiMjKyhJDhw4VY8eOFSdOnBBC5My7cXR0FC9fvpQpN7fnhHK8/c83ICBAmJiYiObNm4uuXbuKHTt2CCGEWLVqlTAwMBDPnj0TQgixfv160aJFC+Hq6ipUVVWFp6enTJnsxXvj7c+HjIwMsXTpUmFkZCRWrlwpDh48KPr06SNGjRoloqKihBBCDBs2TLRr105kZ2eL2NhYsXPnTpGWliZTZnZ2Nt/HVCIYOFG5unv3rkhMTBRCCPH48WNRv359sX79epGamiqEyJloO2LECNG9e3fpMdbW1mL37t3lUt+K7N1v0g4ODkIikYgJEybIpLdv316a9u+//4oJEyaIGjVqiF69egkDAwOxefPmMqvzx+zWrVvit99+E05OTuLkyZPi4cOHwsPDQ2hoaEiH5OrWrSumTJkihBAiISFB/Pnnn2LkyJHi6NGj5Vjziiu/L1RxcXHC29tb3Lx5U5o2dOhQYWxsLL7//nshhBCJiYnCzMxMODo6CmVlZbFs2TIhxJu/CQZMVJIYOFGp+NC3u71794r69euL9u3bi08++UQEBAQIIYT44YcfhI2NjcxckH79+gmJRCJWrVolhBAiJSWlVOv+sVuwYIGoVauWWLx4sejSpYtYvHixzNyO48ePC3Nzc/HXX39J04KDg8XOnTul80RIVn7/0JcuXSokEolwdHSUSe/atasYOHCgEEKII0eOCIlEIr3z823sAZH19rV48uSJqF+/vrS37t69e0IIIX7//Xdhbm4uunTpIiZNmiS6dOkirl+/LoTIGSoNCAgQ9+/fL/vKk0Jh4EQl7u0PwHv37omNGzdKh4KEyPmm3qZNG/H777+Lly9fCg8PD9G8eXPp5NjWrVuLr7/+Wjx8+FCEhYWJcePGiZ9++kmcPHmyrJvyUTlx4oTYunWrGDp0qAgJCRFCCOHv7y86dOggvVU797UZMWKE6Nq1q3QS+Ns4ZPTGu4FNeHi4zO9dunQR/fv3l5kHdu7cOaGnpydevHghhBCic+fOYuPGje8tl3JcvXpVdOzYUcyePVtIJBLh4uIihMgJMsPCwkSXLl2En5+fEEIIHx8foampKUaNGpWnnMzMTM5lolKjuPdrU6lRUlJCWloaxo0bB3t7e4SGhmLz5s149uwZgJzHd6SmpmLw4MHQ09ODp6cnbG1tsXv3bgDAokWLcPfuXfTo0QPNmjVD8+bN8fXXX6Nz587l2awKJXfV77etXbsWY8aMgYWFBaysrAAAw4cPh7GxMY4fP47w8HAoKSkhNTUVy5cvBwCoqanlKUdFRfFWKRFCSB8O+7bcJS0CAwNhaWmJgQMHYvLkyTh//jwAYPLkyfj3339x8eJF6YKgcXFxsLGxkZb3999/w8XFJd9y6Y3Q0FAMGTIEn332GcaNG4cJEyZg8+bNOHv2LCQSCU6cOIF79+5h5MiRSExMxL///otp06ZhxIgRMuUIIaCsrMwVwqn0lHPgRpVQVFSUcHR0FH379pV+63574cVt27aJfv36icjISGnahQsXhIGBgXRScnx8vDh69GieScqK7u1v0ampqeLmzZvSOWJRUVGifv364rvvvpOZGHv69GlhZ2cnpk6dKkaOHCmsra1FQkJCmde9onq79+fu3btiy5Yt0sVT09PTxTfffCMsLCzEzp07xV9//SXGjRsnevfuLe2Z++yzz0Tz5s3F0qVLRUhIiGjRokWeeWXZ2dnsAfm/d4c9c6/L7t27RefOnaXp6enpYvz48aJJkyZCCCFevXolNDU1RY8ePYSampqYMmVKngngRGWBX3uoxJ06dQopKSkICAhAtWrVkJWVBVVVVWkvSbVq1ZCUlIR//vlHeoyenh7MzMzw8uVLAICuri4cHBygp6dXHk2osHK/Ra9YsQL16tXDl19+ifbt2+PPP/9EzZo14eLigoMHD+Lu3bvSYzp27IhvvvkGSUlJUFFRwYkTJ6CjowMg/54rRZPbCzd69Gh069YNV65cwb59+xAREYHk5GQ0aNAAJ0+exKBBg9C1a1fExMTg9OnTWLVqFQBg2bJliI6OxokTJzB//nw4OTnB19dX5hwSiYQ9IHjTGwQAFy5cwMOHD5GSkgIg51l+ycnJ0nyqqqqYNGkS7ty5g23btqFKlSo4f/48xo4diytXrsDb2xtqamp89A+VvXIO3OgjVdC356ysLDF69GgxadIk6e/5HePm5iYcHBzEggULxO3bt4W9vb0YN24cv5XL4eDBg8LW1lZcv35dREVFiTlz5ggLCwsRHBwshBDCyspKzJs3T9oTlevtXr/8JjsrqkePHonOnTsLJycnER8fL4SQneeVu0DomjVrRI0aNcTgwYPF3LlzhbW1tXTflClTxKeffipz5xev8Rtv/10/efJEdO7cWTrJ287OTkRERIgXL14IFRUVmZsWzp8/L6pVqyaMjIzylJmVlcW5YlQu2ONERZLft2chhPTb+5MnT6Rp+R2zfPlyDB48GEFBQRg+fDhatWqFzZs381v5WzIzM/NNP336NAwMDNCsWTPUrFkT33//PRo3boyNGzcCAGbPno2tW7dK5+Hkyn1USnZ2tvRbvyLLfW+eOnUKKioq2L59O3R1dZGVlQUVFRXpHKVatWrhzp072LlzJ3799Vf8/vvv0NTUxM2bN/Htt98CAL7//ns8ffoUhw4dQmJiIgDwGuPNNZZIJNKfV6xYgVatWuHx48f4559/YGBggIkTJyIzMxPffPMNJkyYgO3btyMhIQF+fn7w9PRE7dq1sWzZMmmZuZ81nCtG5YHvOiqSoKAgTJ06VTq0BrwJjHr06IF79+7h5s2bUFZWRmZmJrKysiCEQFJSEiZMmICkpCR8/vnn2LdvH86cOYMff/yxvJpS4eQOn+X+8w4ICEBQUJD0H7kQAnXr1kVSUpL0mK+//hp79+6FEALDhg1D06ZNkZaWlm/5/GeTI/f96ufnh5YtW0JdXR2ZmZnSgEdJSUn6z/7YsWNITExEu3btEBYWhtDQUKxevRqurq4AAC0tLbi5uWHZsmV49OhR+TSoAsq9xqtWrcLMmTMRFxeHgwcPws3NDQCwadMmHD9+HA0bNoSBgQEWL16Mfv36YcuWLbC0tER4eDiGDx8OS0tLREREICsri8OeVO74CUpFcvnyZezZsyfPg2MBwMrKCvXr14eHhweAnAAg9y6X3PlPGRkZAABNTU1oaWmVad0rurfngFSvXh0bNmzA4MGD8cUXX+DBgwdo3749Tp48iX///Vd6zOvXr9G2bVtpb8f+/fvRp0+fcqn/xyI3EDU2NsbTp08B5L2jMPcf9ODBgxH1v/buPK7mfP8D+OtUaKaiorKk8rB16tDILVHGVSqyFFlDWUYayjq26xr7Fu4gg2xjyxIJlaIQKWQMCkVEWZK0KRQ5798f3fPVUYz7u6NO97yff5nv+Xy/Pt+v75zP+3yW9ycrC25ubmjTpg3MzMzg4+OD7t27C8GVr68v1qxZI6xoZOUr5bZt24YTJ05g2LBhKCgogEQiQVhYGMzNzREQEIDIyEisXr0ajx49AgCsXbtW+EEVHh4OPT09ZGRkwNTUlHvxmGKomRFCVht8PN+o4mabqampZGJiQjdu3Kjy3OPHj5Ouri6NHDmSNm7cSBcuXCAXFxcyNjautDcXk5eTk0Njx46lgQMHUnBwMBERxcXF0ahRo2jw4MFEVL7FhLOzM61Zs4bi4uLIzMys0hYePF/sg483iq1o5syZ1K9fPyH31fv374X5Sfn5+TR37lzKy8ujBw8eUFhYmNwms7Jr8rOu/Axkc8eaNm0q5GCTSqVkb29PWlpatH79eqFsYmIieXp6CnnFysrKKD8/n86cOUOOjo7Url07Sk5OrrZ7YexzuMeJVUIV5iUAwOHDhwGUD13Ijr169Qr6+vpyw0UVz+3bty/CwsKgqqqKkJAQzJ07FyYmJrh37x7c3d2r61YUXlXzmHJzc5Gbm4uoqCh06tQJAGBnZ4cBAwYgLy8PFy5cwPr16+Hs7IwzZ85gypQpmDhxotDDJ8PDGeWkUqnw7t65c0cYwpT1OPXr1w85OTnYt28fgPL3XNazcerUKeTk5AAATExM0KdPHxgYGAhDz7JnrMzPWja0LHsGwcHBuHbtGoyMjDBs2DDk5+dDS0tLKDNt2jRYW1sjPT0diYmJ8PPzQ69evdC2bVuYmJgI17179y5mzZoFsViMpKQkSCSSar83xqpUw4EbUyAfbwFx7949Cg4OpmbNmpG7uzulp6fLlW/WrBnt27ePiD6fCbmkpITzBv2JoKAgioqKEvJWnTp1inR1deV65549e0bm5uZ0/Phx4VhRUZHcdXiVUdWKi4tp3Lhx9Le//Y1SU1Mrfb5mzRoyNzcnDw8PCg4OpkuXLlHPnj2pRYsWcs+bfdrBgwdp1apVpKGhQXPmzKGSkhJ6+vQp9e3bl4YOHSpXNjExkSZNmkReXl40ePBgyszMrHS9srKySu83Y4qAAydGRPJLp69evUqtW7em4cOH08uXLyktLY2srKzIyclJWPJORHJpBz6HhzE++DiwiYqKoubNm5ODgwOZmZlRz549hYZ68uTJZGlpKVfeysqKYmJiKl2Xl75/8PH7Fh4eTh4eHjRt2jS5lAxEH/493rx5Q/Hx8dSjRw9ydHQkGxsb8vPz40D0CxQVFVH//v3JxMSEAgMDqVu3btSkSRNheC40NJTat29P4eHhRCT//4BsM28i3iaF1R48VMcAlE9ILioqgoeHBxwcHODl5YW9e/dCS0sLrVq1wo4dO2BiYoIBAwbg6NGjACB0vwOocrsKGWUexqiI/r2EGoAwiXvv3r3w9fVFTEwMQkNDYWNjg7lz56KgoAA//PCDsH1HYGAgLCws8O233+K7776rdG2eNFv+DkqlUrn37f3797hy5QpCQ0PxzTffoE6dOigpKRE+l/171KtXD126dEF0dDQiIiIQFRWF9evXQ0VFhZOEVkBVJJtMSUnBw4cPkZaWBm9vb8TGxqJ169bYs2cP8vPz8fe//x329vZYuXIlAPlVnerq6gA+pMjg7wpWG3DgxAAAN2/eRIsWLZCbm4v8/HzMnTtX7nOJRILAwEC4ublhxYoVWLVqFTp37oyTJ08C4CXuFX0qiBSJRHjz5g369u2LyZMnIzU1FdHR0cLqtzZt2mDo0KEwNDTErl27IJFI4OvrixcvXiArKwv/+Mc/EBsbi4YNG1bn7dQasrw+N27cQGhoqJAOY+zYsXB0dMStW7cAfGisK6rYYKupqaFBgwaQSqVyma6V2cfzmEJDQxEXFwcAePv2LV6/fi3kbgPK95s8fPgwoqOjoa2tjb59++L169eIj4+v8vr8/cFqE35blQx9YjNTAOjSpQtMTU3ljgUHB2Pr1q3COf7+/li4cCEWLVqEwMBAlJWV4d69e1+93rWB7BnJGoGwsDAkJyfjxYsXAID9+/dj69ataNKkCdavXw9TU1Po6Ojg/PnzAMobJ2NjY4hEIqFXxNHREZ06dUJ2djaGDBkC4NOJMZVdaWkpxo4dCwcHBwQFBaFfv35Yt24dmjZtiuHDh6OwsBDBwcEAvqyHtOJiCGUnCx6PHj2KdevWYfTo0Th06BBev34NVVVVNG/eHFevXgVQ/mw7duyIevXqYd++fXj48CG6deuGmJgY2Nra1uRtMPaX4MBJiciSx6moqCA9PR3h4eG4e/cuAEAsFsPT0xOxsbG4fv06cnJyYG9vD19fX+jp6QnBgIaGBpydnRESEgIigqamJho0aFCTt6UwZM8oJCQEJiYmmDdvHkaMGIHx48cDABYsWIBZs2bBxcUFWlpaKC4uxqhRo7BhwwahAVJXV4dUKoWhoSEAwNzcHK6urrh48SJiY2Pl/h5lVlXgc/78eeTl5SE9PR2HDx/G8uXLERAQgL1798LR0RESiQSHDh1CYWGhXHJL9udKSkowevRo+Pj4QF1dHZ07d0ZQUBDi4uJgY2ODZs2aITw8HNHR0VBRUcGlS5fg5OSEtLQ0XLx4EaqqqtDW1v5swMpYrVGTE6xY9SstLaWxY8eSrq4u9ejRg5o0aSKs3MrIyKAJEyaQnp4e6erq0s8///zZaz1//rw6qlwrSKVSevnyJVlbW5OhoSEdPHiQiIhCQkJIIpFQZGQkJSQkkJ6entxKuYcPH5KTkxOJxWJavHgxff/999SpUyfKzMwUJspmZGSQu7s7jR8/vkbuTZFIpdJPToR3c3Ojf/7zn0REdPjwYTI2NiZnZ2d68uQJERFFRkaSnZ0dLV26tNrqW9uUlpbSjBkz6MqVK3IT6VNTU+m7776jvLw84Vjv3r1p8ODB9PLlS7pz5w5NnDiR9PT0qFevXvTNN99QUlISjRkzhnr27FkTt8LYV8OBkxLZsWMHaWpq0sCBA6moqIjevXtH8+bNoxYtWghlTp8+Te3bt6eVK1dWOj8uLk5YLs+qpq2tLTTeROXBZYcOHejYsWNEROTi4kIeHh709OlTocyrV69ow4YNNGHChCqfO1F5gKXsKgZMt27dokWLFlFkZKSwlH3BggXk7u5O3bp1oxYtWtDu3buJiKiwsJDu3r1Lb968oXXr1lFiYmKN1L82+O2330gkEpGVlZXce5yUlEStW7cWkoTKjtWvX5+2bdsmBPmxsbG0Z88eevHiBRERzZgxg0aMGMGrPtn/FA6clIivry/p6OjIHYuMjCQXFxchz1JBQQEtWrSIrK2thR3iIyMjydDQkFxdXeV+cbIPZM/q+PHjpK6uLgRG//rXv8jKyopu375NREQ3btwgIyMjOnjwYKXGpOJSbG5oqlZWVkaHDx8mdXV1GjZsGJmbm5OjoyPl5ORQSEgIGRkZ0ZAhQ4Ty+fn5NHLkSCGIYlWTvXtJSUkkEolo/vz5ZGBgQDNmzKCnT5/S/fv3ycHBgXbu3ElEH1IKGBsbU48ePejatWvCtWQ9VWFhYWRkZCTkemPsfwUHTkqgYt4UHR0dWrduHRERZWdnk1gsJktLSxoyZAhFR0eTVCqlW7duUc+ePWnMmDE0YMAA0tfXp8DAwJqqvkK6ceNGpZ4LWeNja2tLHTt2JIlEQlZWVvTHH3/IfT558mTq1KlTlYkYpVIp57L5hNu3b1PTpk3pxx9/pJs3bxIRUXJyMvXt25cGDBhAROU9HJ06daLNmzfTwoULqVGjRuTl5UXFxcXCdfj5ftrjx4/JwsKCDh06RFFRUdS2bVtydHSk/Px8mjhxIo0YMYKOHDlCRESXL18mT09PkkgktHnzZuEaV69eJTs7O2ratCkFBQXV1K0w9tVw4KQkZD0iv/76K+nq6tK4ceNIR0eHfH196fHjxzR27Fjq3LkzJSYmUllZGa1bt460tLRo0qRJNVxzxfP8+XMyMTGhQ4cOEdGHhljWS/TgwQMSiUQ0ZcoU4ZyP9z/r2rUr3b17t5prXjtUFZQSlb/D1tbWpKOjQ48ePRKe+++//06NGzcWgqnAwEBasGABeXh40OXLl4XzOWD6c9nZ2dS+fXvasmULERHdv3+fOnbsSD169CB/f39auXIlNWzYkPr06UNqamoUHx9Ps2bNImtra7nrnDx5siaqz1i14MBJCXXp0oUaNGggt81BYWEhaWpqUkREBBGVb+9RUFBQU1VUCLKGtmKPneyYnZ0d/fTTT3LHiD4ET97e3mRjY0PFxcVyw248BPd5nwpKZWJjY0lFRYWSkpKEY+np6WRlZfXJrVHev3/PGcC/gOxZ29vbk5+fHxERbdq0iRo3bkxisZjatGlDy5Yto9OnT9O+ffvo0aNHRES0fPlycnV1pdLSUn7OTCnwuuZaLjU1FXv27EFhYSGA8mXan8p0LMv/4+/vj1evXgnZqwEgPj4elpaWaNmyJQDAwMBAadMM0EebHL958wbAhyXw79+/h62tLV6+fIm3b9/K5fqR/fnXX39FSkoKdu3aJZdAseKflTkjtewZV1yeTkTQ09ODoaEhLl++XOV53bp1g6urK/z8/JCbmwugPKHl27dvYWZmVqm8bINfTuHw52TvbteuXXH8+HF06NABGzduxK5duxAfHw8fHx+cOXMG1tbWGDRoEAwNDXHu3Dls2bIFPXv2RN26dfk5M6XAb3ktFxUVhaVLl+L69esAPuzs/urVq0oNs5qaGqRSKWxtbdGrVy/8/PPPePLkCdzd3TFy5EiMGjUKbdu2rYG7UBwVt+zIy8vD9OnT0aNHDxQXFwsJEWVbQ9y7dw9169aVa/xVVFRQVlYGNTU1zJs3D76+vsjJyany71LGjNT/TVAq4+/vj6tXr8LW1hYrVqyAWCyGjY0NDA0NK+Vm4ob8P6elpYXMzExMmDABV65cgZOTE7S1tTF16lRER0dDU1MTaWlp6NGjBwYPHozZs2fDx8enpqvNWPWp0f4u9v9WcQjD2dmZJk2aJKzkSk5OJn19fXrw4EGl82RDRdnZ2SQSiUgkEpG3tzeVlpZWS70VVcUhtKdPn5KnpyeFhYXR5cuXydTUlNzd3SksLEwoExcXRwYGBvTs2bPPXjc0NPRrVbnWqTiMk5ubS9OmTSMbGxsqKiqSKzd79myyt7evdE5Fy5YtIw0NDTp16hRduHDh61VaCd24cYPq1asnfH/I/g0+nst36tSpGqkfYzWNf47VUlThl/WIESNw5coVJCQkQCqVQiKRIDExESYmJpXOU1VVRVlZGfT19REaGorU1FQEBgaibt261Vh7xSPr/Zk9ezbMzMxQVlaGnj17wtraGqGhoWjcuDG8vLwQHR2Nd+/eQVNTE23atEFWVlaV15P9+7i5uVXXLSgsWc+niooKsrKy4OXlhYSEBAwZMgQFBQUYNWoUwsPDhfK9e/fGrVu3kJ2d/ckeo4kTJ6JBgwa4fv06bG1t8e7dO6Ue+vwr1a1bF8bGxnj06BGAD712FbeiAcq3A2JMGXHgVEupqKggOTkZdnZ2uHjxItLS0vDbb7/h4cOHAABjY2O8e/euym0lZEGCq6sr2rRpU53VVlgxMTHQ1dVFcnIyEhISEBQUBDU1NQCAqakp1q9fjzFjxmDGjBlYunQpxGIxkpKSUFRUBKDyFiC8x9kHf3VQCgD169fHsmXLsHDhQrx48QJ16tRRyqHPr6Ft27YICQlB165dq/yc322m7DhwqqWePXsGX19fdO/eHfPmzcOiRYsQFxeHkydPorS0FABQp06dKr/klP2L7+Mgp7i4GJGRkahXrx4iIiIgFouFz+7fv4/S0lKoqKhg1apVmDVrFrZs2YI5c+bAxMQECQkJAHguzef81UGpjJeXF5o1a4Z9+/ZV270oA5FIBIlEAgC8nx9jVeBvewX3qeGHjIwMpKSkYObMmWjcuDF+/PFH+Pj4YPfu3UhJSRHK+fv7IyAgoLqqq9BkDbCKigrevn0rPFtNTU0MGjQI7dq1w6JFiwAA+fn5GDFiBNq1a4eHDx8KDciwYcNw5MgRXLp0CUlJSSgtLeUhogqqOyi9cuUKJk2a9HVuhin9jyzGqsKBk4KSSqUgImH44fz58wgNDcXr168BlDcmZmZmuHnzpnDOokWLcOvWLYSGhiIvLw8A0KRJE3z//ffVfwMKSNYAL126FFZWVvDy8sKqVasAABKJBK6urjhy5AgmT54MU1NTqKmpITs7G23bthUaECKCjY0NduzYgc2bN2PmzJk8RISaC0rr16//Fe+KMcaqUHPz0llVPk6Q+Pr1axo6dCg1bNiQxGIxOTs70507dygrK4vs7e1p+fLl9OrVKyIqTx4oFoupZcuWFBsbWxPVV2ihoaHUqlUr6ty5M0VFRdHq1atJJBIJGadv375N7u7upKGhUeWKxJcvX1Z5XU7698GSJUuoffv2NHz4cPL39ycioqKiItqwYQNZWFjQpEmTSF9fn7y8vCo9T9mqrZSUFAoMDKQ3b95Ue/0ZY+zPcI+TAtm0aRN27twp/AL/5ZdfMHbsWLRv3x4vXrxAdHQ0iAibN2+Gnp4eBg0ahLNnz8LPzw9PnjzB/PnzMXDgQCxduhTdunWr4btRLCkpKRgwYABGjx6NhIQEODs7Y/r06bC3t0dUVBSA8kmxQ4YMQatWreQmJt++fRtOTk7Yv39/pTkfRMTzmwAcPXoUrVu3RkREBPz9/dGhQwfMmjULt27dgqamJuzt7dGqVSts374dly9fxs6dO6GlpSWcX1RUJPTqmZqawtvbG+rq6p+c38QYYzWFv/EVyIULFxAREYHk5GTh2IEDB6Curg4AaNasGUaOHIkbN27g2LFj8PHxwZQpU3Dv3j307dsX6enpmD59OoYMGVJTt6CQiAhisRjdu3fHzZs3hcY4OzsbmZmZ0NfXR2JiIlRUVGBvb4/OnTtj1apVICKMHz8etra2aN++Pby9vSvN+eA5IByUMsaUC38rKRDZ0uqTJ0+ipKQEY8aMgY2NDf744w+hzPDhw2FkZISIiAikpqaiV69eOHHiBKKiohAVFaW026R8jixQ2r59Ow4cOIDY2FgEBATAzMwMRkZGuHLlCnr37o3t27ejYcOGGDp0KK5duwZVVVXk5OQgLS0Nq1evBsCrjD7GQSljTOnU0BAh+4Rly5aRo6OjMEcpPDyctLW16erVq0KZ06dPk4WFBW3durWmqlnryOaOTZkyhUQiEXXs2JESEhKEzz08PMjFxYWIyrNa79q1i37//Xe58z/ecJZ9eK4PHjwgkUhEp0+fpvXr15Ouri45ODiQn58fNWrUiLZt20ZE5Zv0mpiYkEgkov79+1NOTo5wLX6+jLHaQETEP6EVyatXr9C/f39YWFhg9uzZ0NbWxqhRo/D48WOcPXtWKHfq1Ck4OTnVYE0VQ2FhId6+fQs9Pb3PlpNt9goADRs2xPLly+Ht7S18vmTJEqSlpWHXrl2VzgM4T9PnvH//Hqqqqpg6dSrWrVsHS0tLBAQEoHPnzgDKe0kLCgoQERGBvLw8hIeHw9zcHB07dhTOl+0DyBhjio5bAwWjoaGB0aNH4+LFi4iNjYWqqip++uknxMfHY/fu3UI5DprKh4Ps7OwQHByMd+/efbasbPNdAFi8eDHmzJkjJAqdOnUqNm7ciMGDB8udQ/+eY6OsQVNhYeEnNyiuSBbw/PLLL9DR0YG3t7cQNAGAWCxGo0aNAAC6urrw9PREx44dIZVKIZVKhU2TGWOsNlDOFkHBDRs2DEZGRoiMjMS9e/dgYWGBtWvXyiUPVHZEBAMDAzg4OODEiRO4ffu28NmOHTuwc+fOSufIslVPmDABjRs3hqWlJQwNDXH79m1cvnwZvXv3liuvzI05B6WMMVY1/tZSUBMnTkRsbCwuXLgAoLyxt7KyquFaKY63b98CAFasWIHnz5/j5MmTKCgoAAC8fv0aLVu2rPI8WULFLVu2gIiwa9cunDx5Es2bN+cM4P/GQSljjH0az3FSYMePH4eLi4vQKDF5WVlZOHXqFI4dO4bMzEysWbPmi/JXEZFcw01EwpARA0pLS1GvXj2UlJSga9euGDRoELy9vaGtrY0NGzbAwsKiyg1gZXOd4uPjMW7cOAQEBMDBwUHuM8YYq+04cGK10pkzZ+Dm5obhw4ejYcOGWLt2LYYNG4Zly5ZBT08PZWVlXxRwfmk5ZcNBKWOMVY2H6phCk+3Z97GQkBBMmDABmzZtwpIlSxAcHIxz584hLi4OwIeho3Pnzgn7+1WFg6bKzpw5g7Zt2+LSpUswMzNDamoq9u7dK0wUl81nqkrFoKmsrAwikYiDJsbY/xQOnJjCkk0gFolEyMvLk5ukXFBQgKKiIqGci4sL2rRpg61btyIjIwMAcP/+fXh6egpzn5g8DkoZY+w/x4ETq3EfN96y/xaJRCgpKcHAgQPRrVs3uLu7CxOVDQwM8O7dO6Snpwu9HHZ2djh79ixCQkJQXFyMli1bIiMjA02bNq3eG6oFOChljLH/Hw6cWI2TBT6JiYnIy8sT/vvo0aM4cOAANDU1ERwcjOfPn2PJkiXIyMjA4MGDkZaWhj179giNflFREbp27Yr69evj22+/Fa6vzKvlOChljLG/FgdOTCGEhoZizJgxiI2NBQAkJyfD3d0dAQEB8Pf3h1gsxrZt25Cbm4s9e/bAxsYG48aNw+HDh+Hg4IBmzZohJSUF+/fvxw8//CCXH0iZ59hwUMoYY3+xatvchbE/MWjQIPLx8aHMzEwiIpo7dy7p6OjIlVm+fDm5uLgIe/llZmbSkSNHKCIiQigjlUp537MKjhw5Qubm5hQSEkJERElJSaSiokKWlpaUnZ1NRETJycnk5OREixcvJiKioKAgkkgk1LVrV2ratGmlfeUYY0xZcY8TUxg+Pj64fv06YmJiAADjxo2DpqYmVq5cKZQZN24c1NTUsHv3bmRnZ6N58+bo378/XFxcAJT3gIhEIk6wWEH//v1hZmaG6OhoPHr0CO3atcOcOXPw4MED6OvrAwAkEgm6d++Oixcv4ty5c/Dw8MCJEycwdepUbN26FUeOHEGjRo1ARFVOKGeMMWXBgRNTGPb29rC0tERERASSk5NhbGyMOXPmYM2aNSgsLARQvkGvq6srzMzMoKOjU+kayjws9zkclDLG2F+DE2AyhZKWloZRo0bBzc0Nfn5+KC0thZubGwwMDHDgwIGarl6tNnHiRGRnZ2P+/Plo164dNm3ahPnz5yMtLQ0NGjQAUL6lSn5+Pvz8/FC3bt0arjFjjCke7nFiCqV169bo06cPYmJikJiYiAYNGsDPzw/Xrl2rtPRdKpXWTCVrqSlTpiArKwtRUVEoKSmBh4cHzM3NMX78eKHMmDFjMH36dA6aGGPsEzhwYgpn0qRJICIEBQUhNzcXAwYMwJ07d6CtrS1XruLKOfbnOChljLH/Hrc8TOFoaGhg+PDhMDAwgIaGhnCcl77/9zgoZYyx/w7vicAUkpeXV6VjPPH7vycLSu/fv18pKOXnyxhjf44nhzOFJpVKufeDMcaYwuAWiSk0Dpq+Hp7HxBhj/znucWKMMcYY+0L8c54xxhhj7Atx4MQYY4wx9oU4cGKMMcYY+0IcODHGGGOMfSEOnBhjjDHGvhAHTowxxhhjX4gDJ8YYY4yxL8SBE2OMMcbYF+LAiTHGGGPsC/0frzueKMu7NYYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Jaccard similarity heatmap\n",
    "jaccard_matrix = np.zeros((len(labels), len(labels)))\n",
    "for row in overlap_rows:\n",
    "    i, j = labels.index(row[\"A\"]), labels.index(row[\"B\"])\n",
    "    jaccard_matrix[i, j] = row[\"Jaccard\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "im = ax.imshow(jaccard_matrix, cmap=\"YlOrRd\", vmin=0, vmax=1, aspect=\"auto\")\n",
    "ax.set_xticks(range(len(labels)), labels, fontsize=9, rotation=30, ha=\"right\")\n",
    "ax.set_yticks(range(len(labels)), labels, fontsize=9)\n",
    "\n",
    "for i in range(len(labels)):\n",
    "    for j in range(len(labels)):\n",
    "        val = jaccard_matrix[i, j]\n",
    "        color = \"white\" if val > 0.5 else \"black\"\n",
    "        ax.text(j, i, f\"{val:.2f}\", ha=\"center\", va=\"center\", fontsize=10, color=color)\n",
    "\n",
    "ax.set_title(\"Jaccard Similarity of Selected Sub-groups\", fontsize=12, pad=10)\n",
    "fig.colorbar(im, ax=ax, label=\"Jaccard Index\", shrink=0.8)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hpnmj6qoq1",
   "metadata": {},
   "source": [
    "### 5b. Selection order correlation\n",
    "\n",
    "For sub-groups that appear in both an individual selection and the aggregate\n",
    "selection, compare the step at which each was chosen. A high Spearman\n",
    "correlation means the two approaches not only pick the same sub-groups but\n",
    "also prioritise them in a similar order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ttyu3t33xl",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import spearmanr\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
    "\n",
    "for ax, resp in zip(axes, RESPONSES):\n",
    "    ind_key = f\"Ind: {resp}\"\n",
    "    shared = micro_sets[ind_key] & micro_sets[\"Aggregate\"]\n",
    "\n",
    "    steps_ind = [micro_order[ind_key][g] for g in shared]\n",
    "    steps_agg = [micro_order[\"Aggregate\"][g] for g in shared]\n",
    "\n",
    "    ax.scatter(steps_ind, steps_agg, s=20, alpha=0.6)\n",
    "\n",
    "    # Diagonal reference\n",
    "    lo = min(min(steps_ind), min(steps_agg))\n",
    "    hi = max(max(steps_ind), max(steps_agg))\n",
    "    ax.plot([lo, hi], [lo, hi], \"k--\", alpha=0.3, linewidth=1)\n",
    "\n",
    "    rho, pval = spearmanr(steps_ind, steps_agg)\n",
    "    ax.set_title(f\"{resp}\\n({len(shared)} shared, \\u03c1={rho:.3f}, p={pval:.2e})\", fontsize=10)\n",
    "    ax.set_xlabel(f\"Step (Individual)\")\n",
    "    ax.set_ylabel(\"Step (Aggregate)\")\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "fig.suptitle(\"Selection Step: Individual vs Aggregate (shared sub-groups)\", fontsize=13, y=1.02)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nxyz604udq",
   "metadata": {},
   "source": [
    "### 5c. Cross-response overlap within micro individual\n",
    "\n",
    "How much do the three per-DV individual selections agree with each other?\n",
    "Sub-groups in the intersection of all three are strong universal predictors\n",
    "of BEV adoption regardless of how it is measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "w40iwpcsy0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sub-groups selected per individual response:\n",
      "  Ind: BEV Count                  82\n",
      "  Ind: BEV Proportion             79\n",
      "  Ind: BEV per Capita             89\n",
      "\n",
      "In ALL 3 individual selections:  7\n",
      "In ANY individual selection:     197\n",
      "Also in Aggregate selection:     5 of 7\n",
      "\n",
      "=== 7 sub-groups in all 3 individual selections (step number shown) ===\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (7, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Sub-group</th><th>BEV Count</th><th>BEV Proportion</th><th>BEV per Capita</th><th>Aggregate</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;B06010_3_C&quot;</td><td>46</td><td>38</td><td>30</td><td>null</td></tr><tr><td>&quot;B08006_10&quot;</td><td>27</td><td>47</td><td>79</td><td>null</td></tr><tr><td>&quot;B15002_3_C_P1&quot;</td><td>78</td><td>11</td><td>4</td><td>6</td></tr><tr><td>&quot;B25040_2&quot;</td><td>4</td><td>8</td><td>11</td><td>8</td></tr><tr><td>&quot;B15011_8&quot;</td><td>9</td><td>40</td><td>16</td><td>12</td></tr><tr><td>&quot;B19019_2&quot;</td><td>33</td><td>50</td><td>13</td><td>28</td></tr><tr><td>&quot;B19101_2_C_P1&quot;</td><td>76</td><td>24</td><td>78</td><td>72</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (7, 5)\n",
       "┌───────────────┬───────────┬────────────────┬────────────────┬───────────┐\n",
       "│ Sub-group     ┆ BEV Count ┆ BEV Proportion ┆ BEV per Capita ┆ Aggregate │\n",
       "│ ---           ┆ ---       ┆ ---            ┆ ---            ┆ ---       │\n",
       "│ str           ┆ i64       ┆ i64            ┆ i64            ┆ i64       │\n",
       "╞═══════════════╪═══════════╪════════════════╪════════════════╪═══════════╡\n",
       "│ B06010_3_C    ┆ 46        ┆ 38             ┆ 30             ┆ null      │\n",
       "│ B08006_10     ┆ 27        ┆ 47             ┆ 79             ┆ null      │\n",
       "│ B15002_3_C_P1 ┆ 78        ┆ 11             ┆ 4              ┆ 6         │\n",
       "│ B25040_2      ┆ 4         ┆ 8              ┆ 11             ┆ 8         │\n",
       "│ B15011_8      ┆ 9         ┆ 40             ┆ 16             ┆ 12        │\n",
       "│ B19019_2      ┆ 33        ┆ 50             ┆ 13             ┆ 28        │\n",
       "│ B19101_2_C_P1 ┆ 76        ┆ 24             ┆ 78             ┆ 72        │\n",
       "└───────────────┴───────────┴────────────────┴────────────────┴───────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_keys = [f\"Ind: {r}\" for r in RESPONSES]\n",
    "\n",
    "# Pairwise and three-way overlaps\n",
    "all_three = micro_sets[ind_keys[0]] & micro_sets[ind_keys[1]] & micro_sets[ind_keys[2]]\n",
    "any_one = micro_sets[ind_keys[0]] | micro_sets[ind_keys[1]] | micro_sets[ind_keys[2]]\n",
    "\n",
    "print(f\"Sub-groups selected per individual response:\")\n",
    "for k in ind_keys:\n",
    "    print(f\"  {k:30s} {len(micro_sets[k]):>3d}\")\n",
    "print(f\"\\nIn ALL 3 individual selections:  {len(all_three)}\")\n",
    "print(f\"In ANY individual selection:     {len(any_one)}\")\n",
    "print(f\"Also in Aggregate selection:     {len(all_three & micro_sets['Aggregate'])} of {len(all_three)}\")\n",
    "\n",
    "# Show the universal sub-groups with their step in each selection\n",
    "universal_rows = []\n",
    "for g in sorted(all_three):\n",
    "    row = {\"Sub-group\": g}\n",
    "    for k in ind_keys + [\"Aggregate\"]:\n",
    "        row[k.replace(\"Ind: \", \"\")] = micro_order[k].get(g, None)\n",
    "    universal_rows.append(row)\n",
    "\n",
    "universal_df = pl.DataFrame(universal_rows)\n",
    "print(f\"\\n=== {len(universal_df)} sub-groups in all 3 individual selections (step number shown) ===\")\n",
    "universal_df.sort(\"Aggregate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9t77e8ks1c7",
   "metadata": {},
   "source": [
    "### 5d. Unique sub-groups — individual-only vs aggregate-only\n",
    "\n",
    "Sub-groups selected by the aggregate approach but by none of the three\n",
    "individual selections are those that contribute to the *mean* objective\n",
    "without being top-ranked for any single DV. Conversely, sub-groups in\n",
    "an individual selection but absent from the aggregate reveal DV-specific\n",
    "signal that the aggregate criterion trades away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fga93yuvwuq",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregate-only sub-groups (not in any individual selection): 26\n",
      "Individual-only sub-groups (not in aggregate selection):     150\n",
      "\n",
      "--- Aggregate-only (26 sub-groups) ---\n",
      "shape: (26, 2)\n",
      "┌───────────────┬────────────────┐\n",
      "│ Sub-group     ┆ Aggregate Step │\n",
      "│ ---           ┆ ---            │\n",
      "│ str           ┆ i64            │\n",
      "╞═══════════════╪════════════════╡\n",
      "│ B06010_7      ┆ 5              │\n",
      "│ B06009_4      ┆ 17             │\n",
      "│ B08201_5_C_P2 ┆ 19             │\n",
      "│ B08014_3      ┆ 21             │\n",
      "│ B06010_9      ┆ 23             │\n",
      "│ …             ┆ …              │\n",
      "│ B08301_4_P1   ┆ 64             │\n",
      "│ B06010_11_P3  ┆ 66             │\n",
      "│ B08006_5_P1   ┆ 67             │\n",
      "│ B28010_3_P1   ┆ 70             │\n",
      "│ B08016_10     ┆ 71             │\n",
      "└───────────────┴────────────────┘\n",
      "\n",
      "--- Individual-only (150 sub-groups) ---\n",
      "  By 1 response:  129\n",
      "  By 2 responses: 19\n",
      "  By 3 responses: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "agg_only = micro_sets[\"Aggregate\"] - any_one\n",
    "ind_only = any_one - micro_sets[\"Aggregate\"]\n",
    "\n",
    "print(f\"Aggregate-only sub-groups (not in any individual selection): {len(agg_only)}\")\n",
    "print(f\"Individual-only sub-groups (not in aggregate selection):     {len(ind_only)}\")\n",
    "\n",
    "# Aggregate-only: show with their step\n",
    "if agg_only:\n",
    "    agg_only_rows = []\n",
    "    for g in sorted(agg_only):\n",
    "        agg_only_rows.append({\n",
    "            \"Sub-group\": g,\n",
    "            \"Aggregate Step\": micro_order[\"Aggregate\"][g],\n",
    "        })\n",
    "    agg_only_df = pl.DataFrame(agg_only_rows).sort(\"Aggregate Step\")\n",
    "    print(f\"\\n--- Aggregate-only ({len(agg_only_df)} sub-groups) ---\")\n",
    "    print(agg_only_df)\n",
    "\n",
    "# Individual-only: show which response(s) selected it\n",
    "if ind_only:\n",
    "    ind_only_rows = []\n",
    "    for g in sorted(ind_only):\n",
    "        selected_by = [r for r in RESPONSES if g in micro_sets[f\"Ind: {r}\"]]\n",
    "        ind_only_rows.append({\n",
    "            \"Sub-group\": g,\n",
    "            \"Selected by\": \", \".join(selected_by),\n",
    "            \"Count\": len(selected_by),\n",
    "        })\n",
    "    ind_only_df = pl.DataFrame(ind_only_rows).sort(\"Count\", descending=True)\n",
    "    print(f\"\\n--- Individual-only ({len(ind_only_df)} sub-groups) ---\")\n",
    "    print(f\"  By 1 response:  {ind_only_df.filter(pl.col('Count') == 1).height}\")\n",
    "    print(f\"  By 2 responses: {ind_only_df.filter(pl.col('Count') == 2).height}\")\n",
    "    print(f\"  By 3 responses: {ind_only_df.filter(pl.col('Count') == 3).height}\")\n",
    "    print()\n",
    "    ind_only_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ki7qap2qvmg",
   "metadata": {},
   "source": [
    "## 6. Micro-Individual cutoff analysis\n",
    "\n",
    "Each of the three per-DV micro-individual selections runs to a different\n",
    "number of steps (76, 82, 75). To apply a single uniform cutoff, the table\n",
    "below evaluates candidate step limits and reports:\n",
    "\n",
    "- **Macro Groups** — number of distinct ACS table groups (e.g. `B06009`)\n",
    "  represented by the union of sub-groups selected across all 3 responses\n",
    "- **Sub-groups** — number of distinct micro sub-groups in that union\n",
    "- **Total Variables** — number of unique predictor columns covered by those\n",
    "  sub-groups (derived from per-step cumulative predictor deltas)\n",
    "- **Adj R²** — the value reached at that step for each response, plus the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "mmi7eedxtvh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (79, 16)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Cutoff</th><th>Macro Groups</th><th>Sub-groups</th><th>Total Variables</th><th>Base</th><th>Cumul. Only</th><th>Prop. Only</th><th>Both</th><th>P1</th><th>P2</th><th>P3</th><th>P4</th><th>R² (BEV Count)</th><th>R² (BEV Proportion)</th><th>R² (BEV per Capita)</th><th>Mean R²</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>1</td><td>2</td><td>32</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0.9448</td><td>0.7561</td><td>0.6978</td><td>0.7996</td></tr><tr><td>2</td><td>4</td><td>5</td><td>68</td><td>3</td><td>0</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0.9607</td><td>0.7959</td><td>0.7389</td><td>0.8318</td></tr><tr><td>3</td><td>5</td><td>8</td><td>93</td><td>3</td><td>1</td><td>3</td><td>1</td><td>1</td><td>2</td><td>1</td><td>0</td><td>0.9677</td><td>0.8178</td><td>0.7594</td><td>0.8483</td></tr><tr><td>4</td><td>6</td><td>10</td><td>117</td><td>4</td><td>1</td><td>3</td><td>2</td><td>2</td><td>2</td><td>1</td><td>0</td><td>0.9712</td><td>0.8374</td><td>0.7789</td><td>0.8625</td></tr><tr><td>5</td><td>9</td><td>13</td><td>147</td><td>5</td><td>1</td><td>4</td><td>3</td><td>3</td><td>3</td><td>1</td><td>0</td><td>0.9739</td><td>0.855</td><td>0.797</td><td>0.8753</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>75</td><td>39</td><td>178</td><td>1075</td><td>52</td><td>26</td><td>71</td><td>29</td><td>47</td><td>42</td><td>10</td><td>1</td><td>0.9994</td><td>0.9996</td><td>0.9869</td><td>0.9953</td></tr><tr><td>76</td><td>39</td><td>180</td><td>1089</td><td>53</td><td>26</td><td>72</td><td>29</td><td>48</td><td>42</td><td>10</td><td>1</td><td>0.9995</td><td>0.9998</td><td>0.9883</td><td>0.9958</td></tr><tr><td>77</td><td>39</td><td>182</td><td>1100</td><td>53</td><td>26</td><td>74</td><td>29</td><td>48</td><td>44</td><td>10</td><td>1</td><td>0.9996</td><td>0.9999</td><td>0.9897</td><td>0.9964</td></tr><tr><td>78</td><td>39</td><td>183</td><td>1105</td><td>53</td><td>26</td><td>74</td><td>30</td><td>49</td><td>44</td><td>10</td><td>1</td><td>0.9997</td><td>1.0</td><td>0.9914</td><td>0.997</td></tr><tr><td>79</td><td>39</td><td>185</td><td>1109</td><td>53</td><td>27</td><td>75</td><td>30</td><td>50</td><td>44</td><td>10</td><td>1</td><td>0.9998</td><td>1.0</td><td>0.9927</td><td>0.9975</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (79, 16)\n",
       "┌────────┬──────────────┬────────────┬───────────┬───┬────────────────┬─────────────┬─────────────────────┬─────────┐\n",
       "│ Cutoff ┆ Macro Groups ┆ Sub-groups ┆ Total     ┆ … ┆ R² (BEV Count) ┆ R² (BEV     ┆ R² (BEV per Capita) ┆ Mean R² │\n",
       "│ ---    ┆ ---          ┆ ---        ┆ Variables ┆   ┆ ---            ┆ Proportion) ┆ ---                 ┆ ---     │\n",
       "│ i64    ┆ i64          ┆ i64        ┆ ---       ┆   ┆ f64            ┆ ---         ┆ f64                 ┆ f64     │\n",
       "│        ┆              ┆            ┆ i64       ┆   ┆                ┆ f64         ┆                     ┆         │\n",
       "╞════════╪══════════════╪════════════╪═══════════╪═══╪════════════════╪═════════════╪═════════════════════╪═════════╡\n",
       "│ 1      ┆ 1            ┆ 2          ┆ 32        ┆ … ┆ 0.9448         ┆ 0.7561      ┆ 0.6978              ┆ 0.7996  │\n",
       "│ 2      ┆ 4            ┆ 5          ┆ 68        ┆ … ┆ 0.9607         ┆ 0.7959      ┆ 0.7389              ┆ 0.8318  │\n",
       "│ 3      ┆ 5            ┆ 8          ┆ 93        ┆ … ┆ 0.9677         ┆ 0.8178      ┆ 0.7594              ┆ 0.8483  │\n",
       "│ 4      ┆ 6            ┆ 10         ┆ 117       ┆ … ┆ 0.9712         ┆ 0.8374      ┆ 0.7789              ┆ 0.8625  │\n",
       "│ 5      ┆ 9            ┆ 13         ┆ 147       ┆ … ┆ 0.9739         ┆ 0.855       ┆ 0.797               ┆ 0.8753  │\n",
       "│ …      ┆ …            ┆ …          ┆ …         ┆ … ┆ …              ┆ …           ┆ …                   ┆ …       │\n",
       "│ 75     ┆ 39           ┆ 178        ┆ 1075      ┆ … ┆ 0.9994         ┆ 0.9996      ┆ 0.9869              ┆ 0.9953  │\n",
       "│ 76     ┆ 39           ┆ 180        ┆ 1089      ┆ … ┆ 0.9995         ┆ 0.9998      ┆ 0.9883              ┆ 0.9958  │\n",
       "│ 77     ┆ 39           ┆ 182        ┆ 1100      ┆ … ┆ 0.9996         ┆ 0.9999      ┆ 0.9897              ┆ 0.9964  │\n",
       "│ 78     ┆ 39           ┆ 183        ┆ 1105      ┆ … ┆ 0.9997         ┆ 1.0         ┆ 0.9914              ┆ 0.997   │\n",
       "│ 79     ┆ 39           ┆ 185        ┆ 1109      ┆ … ┆ 0.9998         ┆ 1.0         ┆ 0.9927              ┆ 0.9975  │\n",
       "└────────┴──────────────┴────────────┴───────────┴───┴────────────────┴─────────────┴─────────────────────┴─────────┘"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# --- Compute per-sub-group variable counts from cumulative predictor deltas ---\n",
    "subgroup_vars: dict[str, int] = {}\n",
    "\n",
    "for resp in RESPONSES:\n",
    "    sub = micro_ind.filter(pl.col(\"Response\") == resp)\n",
    "    groups = sub[\"Group Added\"].to_list()\n",
    "    preds = sub[\"Cumul. Predictors\"].to_list()\n",
    "    for i, g in enumerate(groups):\n",
    "        if g not in subgroup_vars:\n",
    "            subgroup_vars[g] = preds[i] if i == 0 else preds[i] - preds[i - 1]\n",
    "\n",
    "# --- Sub-group classification helpers ---\n",
    "def classify_subgroup(sg: str) -> dict[str, bool]:\n",
    "    \"\"\"Classify a sub-group ID by its suffix flags.\"\"\"\n",
    "    has_c = \"_C\" in sg and not sg.endswith(\"_C\")  # _C followed by more\n",
    "    has_c |= sg.endswith(\"_C\")                     # _C at end\n",
    "    # More precise: _C is present as a suffix token\n",
    "    has_c = bool(re.search(r\"_C(?:_|$)\", sg))\n",
    "    has_p = bool(re.search(r\"_P\\d\", sg))\n",
    "    return {\"cumul\": has_c, \"prop\": has_p}\n",
    "\n",
    "def prop_level(sg: str) -> str | None:\n",
    "    \"\"\"Extract the proportional level (P1, P2, ...) or None.\"\"\"\n",
    "    m = re.search(r\"_(P\\d)\", sg)\n",
    "    return m.group(1) if m else None\n",
    "\n",
    "# --- Maximum common step ---\n",
    "max_common_step = min(\n",
    "    micro_ind.filter(pl.col(\"Response\") == r)[\"Step\"].max()\n",
    "    for r in RESPONSES\n",
    ")\n",
    "\n",
    "# --- Candidate cutoffs: every step ---\n",
    "cutoffs = list(range(1, max_common_step + 1))\n",
    "\n",
    "# --- Build per-response lookup ---\n",
    "resp_data: dict[str, pl.DataFrame] = {}\n",
    "for resp in RESPONSES:\n",
    "    resp_data[resp] = micro_ind.filter(pl.col(\"Response\") == resp)\n",
    "\n",
    "# --- Discover all proportional levels that appear anywhere ---\n",
    "all_p_levels: set[str] = set()\n",
    "for resp in RESPONSES:\n",
    "    for g in resp_data[resp][\"Group Added\"].to_list():\n",
    "        p = prop_level(g)\n",
    "        if p:\n",
    "            all_p_levels.add(p)\n",
    "p_levels_sorted = sorted(all_p_levels)  # P1, P2, P3, P4\n",
    "\n",
    "# --- Assemble table ---\n",
    "table_rows = []\n",
    "for cutoff in cutoffs:\n",
    "    all_subgroups: set[str] = set()\n",
    "    adj_r2: dict[str, float] = {}\n",
    "\n",
    "    for resp in RESPONSES:\n",
    "        sub = resp_data[resp].filter(pl.col(\"Step\") <= cutoff)\n",
    "        selected = sub[\"Group Added\"].to_list()\n",
    "        all_subgroups.update(selected)\n",
    "        adj_r2[resp] = sub.row(-1, named=True)[\"Adj R\\u00b2\"]\n",
    "\n",
    "    macro_groups = {g.split(\"_\")[0] for g in all_subgroups}\n",
    "    total_vars = sum(subgroup_vars.get(g, 0) for g in all_subgroups)\n",
    "\n",
    "    # Classify sub-groups\n",
    "    classifications = [classify_subgroup(g) for g in all_subgroups]\n",
    "    n_cumul_only = sum(1 for c in classifications if c[\"cumul\"] and not c[\"prop\"])\n",
    "    n_prop_only  = sum(1 for c in classifications if c[\"prop\"] and not c[\"cumul\"])\n",
    "    n_both       = sum(1 for c in classifications if c[\"cumul\"] and c[\"prop\"])\n",
    "    n_base       = sum(1 for c in classifications if not c[\"cumul\"] and not c[\"prop\"])\n",
    "\n",
    "    # Proportional level breakdown (across prop-only AND both)\n",
    "    p_counts: dict[str, int] = {p: 0 for p in p_levels_sorted}\n",
    "    for g in all_subgroups:\n",
    "        p = prop_level(g)\n",
    "        if p:\n",
    "            p_counts[p] += 1\n",
    "\n",
    "    row = {\n",
    "        \"Cutoff\": cutoff,\n",
    "        \"Macro Groups\": len(macro_groups),\n",
    "        \"Sub-groups\": len(all_subgroups),\n",
    "        \"Total Variables\": total_vars,\n",
    "        \"Base\": n_base,\n",
    "        \"Cumul. Only\": n_cumul_only,\n",
    "        \"Prop. Only\": n_prop_only,\n",
    "        \"Both\": n_both,\n",
    "    }\n",
    "    for p in p_levels_sorted:\n",
    "        row[p] = p_counts[p]\n",
    "    for resp in RESPONSES:\n",
    "        row[f\"R\\u00b2 ({resp})\"] = round(adj_r2[resp], 4)\n",
    "    row[\"Mean R\\u00b2\"] = round(np.mean([adj_r2[r] for r in RESPONSES]), 4)\n",
    "    table_rows.append(row)\n",
    "\n",
    "cutoff_table = pl.DataFrame(table_rows)\n",
    "cutoff_table.write_csv(\"CutoffSummaryTable.csv\")\n",
    "cutoff_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "yepjkgyn4h",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cutoff: 40 steps\n",
      "  Unique macro groups: 33\n",
      "  Unique sub-groups:   97\n",
      "\n",
      "Saved SelectedGroups_Cutoff40.csv (33 rows)\n",
      "Saved SelectedSubGroups_Cutoff40.csv (97 rows)\n"
     ]
    }
   ],
   "source": [
    "CUTOFF = 40\n",
    "\n",
    "# Collect all sub-groups selected across the 3 responses up to the cutoff\n",
    "selected_subgroups: set[str] = set()\n",
    "for resp in RESPONSES:\n",
    "    sub = resp_data[resp].filter(pl.col(\"Step\") <= CUTOFF)\n",
    "    selected_subgroups.update(sub[\"Group Added\"].to_list())\n",
    "\n",
    "selected_macro = sorted({g.split(\"_\")[0] for g in selected_subgroups})\n",
    "selected_micro = sorted(selected_subgroups)\n",
    "\n",
    "print(f\"Cutoff: {CUTOFF} steps\")\n",
    "print(f\"  Unique macro groups: {len(selected_macro)}\")\n",
    "print(f\"  Unique sub-groups:   {len(selected_micro)}\")\n",
    "\n",
    "# Save macro groups\n",
    "macro_df = pl.DataFrame({\"Group\": selected_macro})\n",
    "macro_df.write_csv(\"SelectedGroups_Cutoff40.csv\")\n",
    "print(f\"\\nSaved SelectedGroups_Cutoff40.csv ({macro_df.height} rows)\")\n",
    "\n",
    "# Save sub-groups\n",
    "micro_df = pl.DataFrame({\"SubGroup\": selected_micro})\n",
    "micro_df.write_csv(\"SelectedSubGroups_Cutoff40.csv\")\n",
    "print(f\"Saved SelectedSubGroups_Cutoff40.csv ({micro_df.height} rows)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dhy10zqqbu",
   "metadata": {},
   "source": [
    "### Proportional sub-group analysis\n",
    "Each proportional sub-group's `SubGroupID` encodes three pieces of\n",
    "information:\n",
    "| Component | Source | Example |\n",
    "|---|---|---|\n",
    "| **Child level** | The numeric part after the table prefix (e.g. `_3` → level 3) | `B02001_3_P2` → child level 3 |\n",
    "| **P-difference** | The `P#` suffix — how many levels up the denominator sits | `_P2` → denominator is 2 levels above |\n",
    "| **Ancestor level** | Child level − P-difference | 3 − 2 = **level 1** |\n",
    "A `P1` sub-group divides by its immediate parent; a `P2` reaches further\n",
    "up the hierarchy, often to the table total (level 0).  This analysis\n",
    "examines which child levels and ancestor levels the algorithm favours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01l42ctlb7zv",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportional sub-groups at cutoff 40: 48\n",
      "  With cumulative (_C_P#): 14\n",
      "  Proportional only (_P#): 34\n"
     ]
    },
    {
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       "}\n",
       "</style>\n",
       "<small>shape: (48, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>SubGroup</th><th>Macro Group</th><th>Child Level</th><th>Ancestor Level</th><th>P-Difference</th><th>Cumulative</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>bool</td></tr></thead><tbody><tr><td>&quot;B23027_2_C_P1&quot;</td><td>&quot;B23027&quot;</td><td>2</td><td>1</td><td>1</td><td>true</td></tr><tr><td>&quot;B08301_2_P1&quot;</td><td>&quot;B08301&quot;</td><td>2</td><td>1</td><td>1</td><td>false</td></tr><tr><td>&quot;B08202_2_C_P1&quot;</td><td>&quot;B08202&quot;</td><td>2</td><td>1</td><td>1</td><td>true</td></tr><tr><td>&quot;B19101_2_C_P1&quot;</td><td>&quot;B19101&quot;</td><td>2</td><td>1</td><td>1</td><td>true</td></tr><tr><td>&quot;B25104_2_C_P1&quot;</td><td>&quot;B25104&quot;</td><td>2</td><td>1</td><td>1</td><td>true</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;B15011_9_P1&quot;</td><td>&quot;B15011&quot;</td><td>9</td><td>8</td><td>1</td><td>false</td></tr><tr><td>&quot;B15011_10_P1&quot;</td><td>&quot;B15011&quot;</td><td>10</td><td>9</td><td>1</td><td>false</td></tr><tr><td>&quot;B08016_10_P1&quot;</td><td>&quot;B08016&quot;</td><td>10</td><td>9</td><td>1</td><td>false</td></tr><tr><td>&quot;B08006_10_P1&quot;</td><td>&quot;B08006&quot;</td><td>10</td><td>9</td><td>1</td><td>false</td></tr><tr><td>&quot;B08006_13_P2&quot;</td><td>&quot;B08006&quot;</td><td>13</td><td>11</td><td>2</td><td>false</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (48, 6)\n",
       "┌───────────────┬─────────────┬─────────────┬────────────────┬──────────────┬────────────┐\n",
       "│ SubGroup      ┆ Macro Group ┆ Child Level ┆ Ancestor Level ┆ P-Difference ┆ Cumulative │\n",
       "│ ---           ┆ ---         ┆ ---         ┆ ---            ┆ ---          ┆ ---        │\n",
       "│ str           ┆ str         ┆ i64         ┆ i64            ┆ i64          ┆ bool       │\n",
       "╞═══════════════╪═════════════╪═════════════╪════════════════╪══════════════╪════════════╡\n",
       "│ B23027_2_C_P1 ┆ B23027      ┆ 2           ┆ 1              ┆ 1            ┆ true       │\n",
       "│ B08301_2_P1   ┆ B08301      ┆ 2           ┆ 1              ┆ 1            ┆ false      │\n",
       "│ B08202_2_C_P1 ┆ B08202      ┆ 2           ┆ 1              ┆ 1            ┆ true       │\n",
       "│ B19101_2_C_P1 ┆ B19101      ┆ 2           ┆ 1              ┆ 1            ┆ true       │\n",
       "│ B25104_2_C_P1 ┆ B25104      ┆ 2           ┆ 1              ┆ 1            ┆ true       │\n",
       "│ …             ┆ …           ┆ …           ┆ …              ┆ …            ┆ …          │\n",
       "│ B15011_9_P1   ┆ B15011      ┆ 9           ┆ 8              ┆ 1            ┆ false      │\n",
       "│ B15011_10_P1  ┆ B15011      ┆ 10          ┆ 9              ┆ 1            ┆ false      │\n",
       "│ B08016_10_P1  ┆ B08016      ┆ 10          ┆ 9              ┆ 1            ┆ false      │\n",
       "│ B08006_10_P1  ┆ B08006      ┆ 10          ┆ 9              ┆ 1            ┆ false      │\n",
       "│ B08006_13_P2  ┆ B08006      ┆ 13          ┆ 11             ┆ 2            ┆ false      │\n",
       "└───────────────┴─────────────┴─────────────┴────────────────┴──────────────┴────────────┘"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Parse proportional sub-groups at cutoff 40 ---\n",
    "prop_rows = []\n",
    "for g in sorted(selected_subgroups):\n",
    "    p_match = re.search(r\"_P(\\d)\", g)\n",
    "    if not p_match:\n",
    "        continue\n",
    "    p_diff = int(p_match.group(1))\n",
    "\n",
    "    # Child level: first digit after {TableID}_ (e.g. B02001_3_C_P2 → 3)\n",
    "    level_match = re.match(r\"[A-Z]\\d{5}_(\\d+)|([A-Z]+)_(\\d+)\", g)\n",
    "    if level_match:\n",
    "        child_level = int(level_match.group(1) or level_match.group(3))\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "    ancestor_level = child_level - p_diff\n",
    "    has_c = bool(re.search(r\"_C(?:_|$)\", g))\n",
    "\n",
    "    prop_rows.append({\n",
    "        \"SubGroup\": g,\n",
    "        \"Macro Group\": g.split(\"_\")[0],\n",
    "        \"Child Level\": child_level,\n",
    "        \"Ancestor Level\": ancestor_level,\n",
    "        \"P-Difference\": p_diff,\n",
    "        \"Cumulative\": has_c,\n",
    "    })\n",
    "\n",
    "prop_df = pl.DataFrame(prop_rows)\n",
    "print(f\"Proportional sub-groups at cutoff {CUTOFF}: {prop_df.height}\")\n",
    "print(f\"  With cumulative (_C_P#): {prop_df.filter(pl.col('Cumulative')).height}\")\n",
    "print(f\"  Proportional only (_P#): {prop_df.filter(~pl.col('Cumulative')).height}\")\n",
    "prop_df.sort(\"Child Level\", \"Ancestor Level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ins3mbrcjf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== By P-Difference ===\n",
      "shape: (3, 2)\n",
      "┌──────────────┬───────┐\n",
      "│ P-Difference ┆ Count │\n",
      "│ ---          ┆ ---   │\n",
      "│ i64          ┆ u32   │\n",
      "╞══════════════╪═══════╡\n",
      "│ 1            ┆ 24    │\n",
      "│ 2            ┆ 20    │\n",
      "│ 3            ┆ 4     │\n",
      "└──────────────┴───────┘\n",
      "\n",
      "=== By Child Level ===\n",
      "shape: (10, 2)\n",
      "┌─────────────┬───────┐\n",
      "│ Child Level ┆ Count │\n",
      "│ ---         ┆ ---   │\n",
      "│ i64         ┆ u32   │\n",
      "╞═════════════╪═══════╡\n",
      "│ 2           ┆ 5     │\n",
      "│ 3           ┆ 10    │\n",
      "│ 4           ┆ 4     │\n",
      "│ 5           ┆ 4     │\n",
      "│ 6           ┆ 6     │\n",
      "│ 7           ┆ 10    │\n",
      "│ 8           ┆ 3     │\n",
      "│ 9           ┆ 2     │\n",
      "│ 10          ┆ 3     │\n",
      "│ 13          ┆ 1     │\n",
      "└─────────────┴───────┘\n",
      "\n",
      "=== By Ancestor Level ===\n",
      "shape: (10, 2)\n",
      "┌────────────────┬───────┐\n",
      "│ Ancestor Level ┆ Count │\n",
      "│ ---            ┆ ---   │\n",
      "│ i64            ┆ u32   │\n",
      "╞════════════════╪═══════╡\n",
      "│ 1              ┆ 11    │\n",
      "│ 2              ┆ 7     │\n",
      "│ 3              ┆ 3     │\n",
      "│ 4              ┆ 8     │\n",
      "│ 5              ┆ 7     │\n",
      "│ 6              ┆ 5     │\n",
      "│ 7              ┆ 2     │\n",
      "│ 8              ┆ 1     │\n",
      "│ 9              ┆ 3     │\n",
      "│ 11             ┆ 1     │\n",
      "└────────────────┴───────┘\n"
     ]
    }
   ],
   "source": [
    "# --- Distribution summaries ---\n",
    "print(\"=== By P-Difference ===\")\n",
    "print(prop_df.group_by(\"P-Difference\").agg(pl.len().alias(\"Count\")).sort(\"P-Difference\"))\n",
    "\n",
    "print(\"\\n=== By Child Level ===\")\n",
    "print(prop_df.group_by(\"Child Level\").agg(pl.len().alias(\"Count\")).sort(\"Child Level\"))\n",
    "\n",
    "print(\"\\n=== By Ancestor Level ===\")\n",
    "print(prop_df.group_by(\"Ancestor Level\").agg(pl.len().alias(\"Count\")).sort(\"Ancestor Level\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "y0f69qzir5i",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Cross-tabulation: Child Level vs Ancestor Level ---\n",
    "child_levels = sorted(prop_df[\"Child Level\"].unique().to_list())\n",
    "ancestor_levels = sorted(prop_df[\"Ancestor Level\"].unique().to_list())\n",
    "\n",
    "cross_tab = np.zeros((len(child_levels), len(ancestor_levels)), dtype=int)\n",
    "for row in prop_df.iter_rows(named=True):\n",
    "    ci = child_levels.index(row[\"Child Level\"])\n",
    "    ai = ancestor_levels.index(row[\"Ancestor Level\"])\n",
    "    cross_tab[ci, ai] += 1\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "# (a) Child Level vs Ancestor Level heatmap\n",
    "ax = axes[0]\n",
    "im = ax.imshow(cross_tab, cmap=\"Blues\", aspect=\"auto\")\n",
    "ax.set_xticks(range(len(ancestor_levels)), [str(a) for a in ancestor_levels])\n",
    "ax.set_yticks(range(len(child_levels)), [str(c) for c in child_levels])\n",
    "ax.set_xlabel(\"Ancestor Level\")\n",
    "ax.set_ylabel(\"Child Level\")\n",
    "ax.set_title(\"Child Level vs Ancestor Level\", fontsize=11)\n",
    "for i in range(len(child_levels)):\n",
    "    for j in range(len(ancestor_levels)):\n",
    "        v = cross_tab[i, j]\n",
    "        if v > 0:\n",
    "            color = \"white\" if v > cross_tab.max() / 2 else \"black\"\n",
    "            ax.text(j, i, str(v), ha=\"center\", va=\"center\", fontsize=10, color=color)\n",
    "\n",
    "# (b) Bar chart by P-Difference, split by cumulative flag\n",
    "ax = axes[1]\n",
    "p_diffs = sorted(prop_df[\"P-Difference\"].unique().to_list())\n",
    "counts_cum = []\n",
    "counts_prop = []\n",
    "for p in p_diffs:\n",
    "    sub = prop_df.filter(pl.col(\"P-Difference\") == p)\n",
    "    counts_cum.append(sub.filter(pl.col(\"Cumulative\")).height)\n",
    "    counts_prop.append(sub.filter(~pl.col(\"Cumulative\")).height)\n",
    "\n",
    "x = np.arange(len(p_diffs))\n",
    "ax.bar(x - 0.2, counts_prop, 0.4, label=\"Prop. only\", alpha=0.8)\n",
    "ax.bar(x + 0.2, counts_cum, 0.4, label=\"Cumul. + Prop.\", alpha=0.8)\n",
    "ax.set_xticks(x, [f\"P{p}\" for p in p_diffs])\n",
    "ax.set_xlabel(\"P-Difference\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_title(\"P-Difference by Cumulative Flag\", fontsize=11)\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, alpha=0.3, axis=\"y\")\n",
    "\n",
    "# (c) Child Level distribution, split by P-Difference\n",
    "ax = axes[2]\n",
    "bottom = np.zeros(len(child_levels))\n",
    "for p in p_diffs:\n",
    "    counts = []\n",
    "    for cl in child_levels:\n",
    "        counts.append(prop_df.filter(\n",
    "            (pl.col(\"Child Level\") == cl) & (pl.col(\"P-Difference\") == p)\n",
    "        ).height)\n",
    "    ax.bar(range(len(child_levels)), counts, bottom=bottom, label=f\"P{p}\", alpha=0.8)\n",
    "    bottom += np.array(counts)\n",
    "\n",
    "ax.set_xticks(range(len(child_levels)), [str(c) for c in child_levels])\n",
    "ax.set_xlabel(\"Child Level\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_title(\"Child Level by P-Difference\", fontsize=11)\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, alpha=0.3, axis=\"y\")\n",
    "\n",
    "fig.suptitle(f\"Proportional Sub-groups at Cutoff {CUTOFF} ({prop_df.height} sub-groups)\", fontsize=13, y=1.02)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "vhgq2dskujc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Proportional sub-groups by macro group (20 groups) ===\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (20, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Macro Group</th><th>Count</th><th>Mean Child Level</th><th>Mean Ancestor Level</th><th>Mean P-Diff</th><th># Cumul.</th><th># P1</th><th># P2</th><th># P3+</th></tr><tr><td>str</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td></tr></thead><tbody><tr><td>&quot;B15011&quot;</td><td>6</td><td>8.166667</td><td>6.666667</td><td>1.5</td><td>1</td><td>3</td><td>3</td><td>0</td></tr><tr><td>&quot;B08016&quot;</td><td>5</td><td>6.0</td><td>4.4</td><td>1.6</td><td>0</td><td>3</td><td>1</td><td>1</td></tr><tr><td>&quot;B15002&quot;</td><td>4</td><td>3.5</td><td>2.0</td><td>1.5</td><td>3</td><td>2</td><td>2</td><td>0</td></tr><tr><td>&quot;B08006&quot;</td><td>4</td><td>9.25</td><td>7.25</td><td>2.0</td><td>0</td><td>1</td><td>2</td><td>1</td></tr><tr><td>&quot;B06010&quot;</td><td>3</td><td>7.0</td><td>5.0</td><td>2.0</td><td>2</td><td>1</td><td>1</td><td>1</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;B17020&quot;</td><td>1</td><td>3.0</td><td>2.0</td><td>1.0</td><td>1</td><td>1</td><td>0</td><td>0</td></tr><tr><td>&quot;B19101&quot;</td><td>1</td><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>1</td><td>0</td><td>0</td></tr><tr><td>&quot;B25104&quot;</td><td>1</td><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>1</td><td>0</td><td>0</td></tr><tr><td>&quot;B06012&quot;</td><td>1</td><td>6.0</td><td>5.0</td><td>1.0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>&quot;B08301&quot;</td><td>1</td><td>2.0</td><td>1.0</td><td>1.0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (20, 9)\n",
       "┌─────────────┬───────┬──────────────────┬─────────────────────┬───┬──────────┬──────┬──────┬───────┐\n",
       "│ Macro Group ┆ Count ┆ Mean Child Level ┆ Mean Ancestor Level ┆ … ┆ # Cumul. ┆ # P1 ┆ # P2 ┆ # P3+ │\n",
       "│ ---         ┆ ---   ┆ ---              ┆ ---                 ┆   ┆ ---      ┆ ---  ┆ ---  ┆ ---   │\n",
       "│ str         ┆ u32   ┆ f64              ┆ f64                 ┆   ┆ u32      ┆ u32  ┆ u32  ┆ u32   │\n",
       "╞═════════════╪═══════╪══════════════════╪═════════════════════╪═══╪══════════╪══════╪══════╪═══════╡\n",
       "│ B15011      ┆ 6     ┆ 8.166667         ┆ 6.666667            ┆ … ┆ 1        ┆ 3    ┆ 3    ┆ 0     │\n",
       "│ B08016      ┆ 5     ┆ 6.0              ┆ 4.4                 ┆ … ┆ 0        ┆ 3    ┆ 1    ┆ 1     │\n",
       "│ B15002      ┆ 4     ┆ 3.5              ┆ 2.0                 ┆ … ┆ 3        ┆ 2    ┆ 2    ┆ 0     │\n",
       "│ B08006      ┆ 4     ┆ 9.25             ┆ 7.25                ┆ … ┆ 0        ┆ 1    ┆ 2    ┆ 1     │\n",
       "│ B06010      ┆ 3     ┆ 7.0              ┆ 5.0                 ┆ … ┆ 2        ┆ 1    ┆ 1    ┆ 1     │\n",
       "│ …           ┆ …     ┆ …                ┆ …                   ┆ … ┆ …        ┆ …    ┆ …    ┆ …     │\n",
       "│ B17020      ┆ 1     ┆ 3.0              ┆ 2.0                 ┆ … ┆ 1        ┆ 1    ┆ 0    ┆ 0     │\n",
       "│ B19101      ┆ 1     ┆ 2.0              ┆ 1.0                 ┆ … ┆ 1        ┆ 1    ┆ 0    ┆ 0     │\n",
       "│ B25104      ┆ 1     ┆ 2.0              ┆ 1.0                 ┆ … ┆ 1        ┆ 1    ┆ 0    ┆ 0     │\n",
       "│ B06012      ┆ 1     ┆ 6.0              ┆ 5.0                 ┆ … ┆ 0        ┆ 1    ┆ 0    ┆ 0     │\n",
       "│ B08301      ┆ 1     ┆ 2.0              ┆ 1.0                 ┆ … ┆ 0        ┆ 1    ┆ 0    ┆ 0     │\n",
       "└─────────────┴───────┴──────────────────┴─────────────────────┴───┴──────────┴──────┴──────┴───────┘"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Per-macro-group breakdown ---\n",
    "macro_prop = (\n",
    "    prop_df\n",
    "    .group_by(\"Macro Group\")\n",
    "    .agg(\n",
    "        pl.len().alias(\"Count\"),\n",
    "        pl.col(\"Child Level\").mean().alias(\"Mean Child Level\"),\n",
    "        pl.col(\"Ancestor Level\").mean().alias(\"Mean Ancestor Level\"),\n",
    "        pl.col(\"P-Difference\").mean().alias(\"Mean P-Diff\"),\n",
    "        pl.col(\"Cumulative\").sum().alias(\"# Cumul.\"),\n",
    "        (pl.col(\"P-Difference\") == 1).sum().alias(\"# P1\"),\n",
    "        (pl.col(\"P-Difference\") == 2).sum().alias(\"# P2\"),\n",
    "        (pl.col(\"P-Difference\") >= 3).sum().alias(\"# P3+\"),\n",
    "    )\n",
    "    .sort(\"Count\", descending=True)\n",
    ")\n",
    "\n",
    "print(f\"=== Proportional sub-groups by macro group ({macro_prop.height} groups) ===\")\n",
    "macro_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "etjsk1lxii",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full dictionary: 3254 rows\n",
      "\n",
      "Filtered by 33 macro groups: 2927 rows\n",
      "  Saved ACSEnhanced_Dictionary_SelectedGroups.csv\n",
      "\n",
      "Filtered by 97 sub-groups: 642 rows\n",
      "  Saved ACSEnhanced_Dictionary_SelectedSubGroups.csv\n"
     ]
    }
   ],
   "source": [
    "# --- Load full dictionary and filter to selected groups / sub-groups ---\n",
    "dictionary = pl.read_csv(\"ACSEnhanced_Dictionary.csv\")\n",
    "print(f\"Full dictionary: {dictionary.height} rows\")\n",
    "\n",
    "# Filter by selected macro groups\n",
    "dict_by_group = dictionary.filter(pl.col(\"Group\").is_in(selected_macro))\n",
    "dict_by_group.write_csv(\"ACSEnhanced_Dictionary_SelectedGroups.csv\")\n",
    "print(f\"\\nFiltered by {len(selected_macro)} macro groups: {dict_by_group.height} rows\")\n",
    "print(f\"  Saved ACSEnhanced_Dictionary_SelectedGroups.csv\")\n",
    "\n",
    "# Filter by selected sub-groups\n",
    "dict_by_subgroup = dictionary.filter(pl.col(\"SubGroupID\").is_in(selected_micro))\n",
    "dict_by_subgroup.write_csv(\"ACSEnhanced_Dictionary_SelectedSubGroups.csv\")\n",
    "print(f\"\\nFiltered by {len(selected_micro)} sub-groups: {dict_by_subgroup.height} rows\")\n",
    "print(f\"  Saved ACSEnhanced_Dictionary_SelectedSubGroups.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "t5k8klir3gs",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved SelectedSubGroups_Cutoff40_Described.csv (97 rows)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (97, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>SubGroupID</th><th>Description</th></tr><tr><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;B01002_2&quot;</td><td>&quot;Age by sex&quot;</td></tr><tr><td>&quot;B02001_2&quot;</td><td>&quot;Population by race&quot;</td></tr><tr><td>&quot;B05002_5_P1&quot;</td><td>&quot;Proportion of population (Nati…</td></tr><tr><td>&quot;B05002_7&quot;</td><td>&quot;Population (Foreign-born &gt; Nat…</td></tr><tr><td>&quot;B05002_7_P3&quot;</td><td>&quot;Proportion of population (Fore…</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;C24050_3_P2&quot;</td><td>&quot;Proportion of population (Mana…</td></tr><tr><td>&quot;C24050_4&quot;</td><td>&quot;Population (Service occupation…</td></tr><tr><td>&quot;C24050_5&quot;</td><td>&quot;Population (Sales and office o…</td></tr><tr><td>&quot;C24050_7&quot;</td><td>&quot;Population (Production, transp…</td></tr><tr><td>&quot;C24050_7_P2&quot;</td><td>&quot;Proportion of population (Prod…</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (97, 2)\n",
       "┌─────────────┬─────────────────────────────────┐\n",
       "│ SubGroupID  ┆ Description                     │\n",
       "│ ---         ┆ ---                             │\n",
       "│ str         ┆ str                             │\n",
       "╞═════════════╪═════════════════════════════════╡\n",
       "│ B01002_2    ┆ Age by sex                      │\n",
       "│ B02001_2    ┆ Population by race              │\n",
       "│ B05002_5_P1 ┆ Proportion of population (Nati… │\n",
       "│ B05002_7    ┆ Population (Foreign-born > Nat… │\n",
       "│ B05002_7_P3 ┆ Proportion of population (Fore… │\n",
       "│ …           ┆ …                               │\n",
       "│ C24050_3_P2 ┆ Proportion of population (Mana… │\n",
       "│ C24050_4    ┆ Population (Service occupation… │\n",
       "│ C24050_5    ┆ Population (Sales and office o… │\n",
       "│ C24050_7    ┆ Population (Production, transp… │\n",
       "│ C24050_7_P2 ┆ Proportion of population (Prod… │\n",
       "└─────────────┴─────────────────────────────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Build a description for each selected sub-group ---\n",
    "\n",
    "def describe_subgroup(sg: str, rows: pl.DataFrame) -> str:\n",
    "    \"\"\"Generate a human-readable description from dictionary metadata.\"\"\"\n",
    "    first = rows.row(0, named=True)\n",
    "    measure = first[\"WhatTheVariableMeasures\"] or \"\"\n",
    "    partition = first[\"Partition\"] or \"\"\n",
    "    is_cumul = first[\"Cumulative\"]\n",
    "    is_prop = first[\"Proportional\"]\n",
    "    level = int(first[\"Level\"]) if first[\"Level\"] is not None else 0\n",
    "\n",
    "    # Simplify common measure names\n",
    "    measure_text = {\n",
    "        \"Number of People\": \"population\",\n",
    "        \"Dollars\": \"income\",\n",
    "        \"Percentage\": \"percentage\",\n",
    "    }.get(measure, measure.lower())\n",
    "\n",
    "    # Parent context: Label_2 through Label_{level} (above the varying dimension)\n",
    "    parent_parts = []\n",
    "    for i in range(2, level + 1):\n",
    "        col = f\"Label_{i}\"\n",
    "        val = first.get(col)\n",
    "        if val and str(val) not in (\"\", \"None\", \"null\"):\n",
    "            clean = str(val).rstrip(\":\").strip()\n",
    "            if clean.lower() not in (\"total\", \"total population\", \"\"):\n",
    "                parent_parts.append(clean)\n",
    "\n",
    "    # Build prefix\n",
    "    if is_cumul and is_prop:\n",
    "        prefix = \"Cumulative proportion of\"\n",
    "    elif is_cumul:\n",
    "        prefix = \"Cumulative\"\n",
    "    elif is_prop:\n",
    "        prefix = \"Proportion of\"\n",
    "    else:\n",
    "        prefix = \"\"\n",
    "\n",
    "    # Build core\n",
    "    if parent_parts:\n",
    "        ctx = \" > \".join(parent_parts)\n",
    "        if partition:\n",
    "            core = f\"{measure_text} ({ctx}) by {partition.lower()}\"\n",
    "        else:\n",
    "            core = f\"{measure_text} ({ctx})\"\n",
    "    else:\n",
    "        if partition:\n",
    "            core = f\"{measure_text} by {partition.lower()}\"\n",
    "        else:\n",
    "            core = measure_text\n",
    "\n",
    "    if prefix:\n",
    "        return f\"{prefix} {core}\"\n",
    "    return core[0].upper() + core[1:]\n",
    "\n",
    "\n",
    "desc_rows = []\n",
    "for sg in sorted(selected_micro):\n",
    "    rows = dict_by_subgroup.filter(pl.col(\"SubGroupID\") == sg)\n",
    "    if rows.height == 0:\n",
    "        continue\n",
    "    desc_rows.append({\n",
    "        \"SubGroupID\": sg,\n",
    "        \"Description\": describe_subgroup(sg, rows),\n",
    "    })\n",
    "\n",
    "desc_df = pl.DataFrame(desc_rows)\n",
    "desc_df.write_csv(\"SelectedSubGroups_Cutoff40_Described.csv\")\n",
    "print(f\"Saved SelectedSubGroups_Cutoff40_Described.csv ({desc_df.height} rows)\\n\")\n",
    "desc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eed98ef6-83f5-4bc0-8b06-f5815a6af52a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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